Stochastic Tools System Design Description

This template follows INL template TEM-140, "IT System Design Description."

note

This document serves as an addendum to Framework System Design Description and captures information for Software Design Description (SDD) specific to the Stochastic Tools module.

Framework System Design Description

Introduction

The MOOSE Stochastic Tools module is based on the MOOSE framework and thus inherits the unique features and base characteristics of the framework, as outlined in the Framework System Design Description. Specific details unique to the module are outlined in this document.

System Purpose

The Software Design Description provided here is description of each object in the system. The pluggable architecture of the underlying framework of the Stochastic Tools module makes MOOSE and MOOSE-based applications straightforward to develop as each piece of end-user (developer) code that goes into the system follows a well-defined interface for the underlying systems that those object plug into. These descriptions are provided through developer-supplied "markdown" files that are required for all new objects that are developed as part of the Stochastic Tools module. More information about the design documentation for MOOSE-based applications and like the Stochastic Tools module can be found in Documenting MOOSE.

System Scope

The MOOSE Stochastic Tools module builds on the existing framework MultiApps system and provides several additional systems to address its purpose. To address the efficient evaluation of multiphysics models, the MultiApps system is extended to include a "batch" mode of execution. This mode provides a memory efficient way of building MooseApp instances and reusing them for different perturbations of input parameters. This is useful for stochastic simulations with many, many samples to be run without holding them in memory or re-initializing potentially costly data allocation. The additional systems that the Stochastic Tools module provides include: Distributions, Samplers, Trainers, and Surrogates. Distributions are functions defining the uncertainty of input parameters and provides an interface for computing probability density, cumulative probability, and quantiles. Samplers define the sampling scheme of the stochastic analysis, whether it be random or deterministic. Trainers are objects that build meta-models meant to be used as surrogates or reduced-order models of the multiphysics model. The resulting reduced model is able to be saved in a meta-data file and reloaded for future use. Surrogates take the data created from a Trainer and provide functionality to evaluate the model. The module also provides capabilities for computing quantities related to basic uncertainty quantification and sensitivity analysis using the framework's Reporters system.

Dependencies and Limitations

The Stochastic Tools module inherits the software dependencies of the MOOSE framework, with no additional dependencies. The design of this module is motivated by the needs of its client applications.

Definitions and Acronyms

This section defines, or provides the definition of, all terms and acronyms required to properly understand this specification.

Definitions

Pull (Merge) Request: A proposed change to the software (e.g. usually a code change, but may also include documentation, requirements, design, and/or testing).
Baseline: A specification or product (e.g., project plan, maintenance and operations (M&O) plan, requirements, or design) that has been formally reviewed and agreed upon, that thereafter serves as the basis for use and further development, and that can be changed only by using an approved change control process (NQA-1, 2009).
Validation: Confirmation, through the provision of objective evidence (e.g., acceptance test), that the requirements for a specific intended use or application have been fulfilled (24765:2010(E), 2010).
Verification: (1) The process of: evaluating a system or component to determine whether the products of a given development phase satisfy the conditions imposed at the start of that phase. (2) Formal proof of program correctness (e.g., requirements, design, implementation reviews, system tests) (24765:2010(E), 2010).

Acronyms

Acronym	Description
API	Application Programming Interface
DOE-NE	Department of Energy, Nuclear Energy
FE	finite element
HIT	Hierarchical Input Text
HPC	High Performance Computing
I/O	Input/Output
INL	Idaho National Laboratory
MOOSE	Multiphysics Object Oriented Simulation Environment
MPI	Message Passing Interface
SDD	Software Design Description

Design Stakeholders and Concerns

Design Stakeholders

Stakeholders for MOOSE include several of the funding sources including Department of Energy, Nuclear Energy (DOE-NE) and the INL. However, Since MOOSE is an open-source project, several universities, companies, and foreign governments have an interest in the development and maintenance of the MOOSE project.

Stakeholder Design Concerns

Concerns from many of the stakeholders are similar. These concerns include correctness, stability, and performance. The mitigation plan for each of these can be addressed. For correctness, Stochastic Tools module development requires either regression or unit testing for all new code added to the repository. The project contains several comparisons against analytical solutions where possible and also other verification methods such as MMS. For stability, the Stochastic Tools module (located within the MOOSE repository) maintains multiple branches to incorporate several layers of testing both internally and for dependent applications. Finally, performance tests are also performed as part of the the normal testing suite to monitor code change impacts to performance.

System Design

The Stochastic Tools module inherits the wide range of pluggable systems from MOOSE. More information regarding MOOSE system design can be found in the framework System Design section. Most of these inherited systems are utilized to build physics models for which stochastic analysis is being performed. Other than these, the inherited core capabilities define the order and frequency of object execution. Furthermore, the MultiApps, Transfers, Controls, and Reporters systems in the framework are utilized to partition the stochastic simulations and manipulate distributed data. The Stochastic Tools implements several other pluggable systems, including: Distributions, Samplers, Trainers, and Surrogates. Distributions and Samplers define the parameter space of the stochastic analysis and Trainers and Surrogates build and utilize the data from the analysis to build reduced-order models. Dependent applications can inherit from these systems to define new methods. Documentation for each object, data structure, and process specific to the module are kept up-to-date alongside the MOOSE documentation. Expected failure modes and error conditions are accounted for via regression testing, and error conditions are noted in object documentation where applicable.

System Structure

The architecture of the Stochastic Tools module consists of a core and several pluggable systems. The core of MOOSE consists of a number of key objects responsible for setting up and managing the user-defined objects of a finite element simulation. This core set of objects has limited extendability and exists for every simulation configuration that the module is capable of running.

AuxKernels

AuxScalarKernels

AuxVariables

Controls

Covariance

Distributions

Functions

Likelihood

MultiApps

Outputs

ParameterStudy

Postprocessors

Reporters

Samplers

StochasticTools

Surrogates

Trainers

Transfers

UserObjects

VariableMappings

VectorPostprocessors

The MooseApp is the top-level object used to hold all of the other objects in a simulation. In a normal simulation, a single MooseApp object is created and "run()". This object uses its Factory objects to build user-defined objects which are stored in a series of Warehouse objects and executed. The Finite Element data is stored in the Systems and Assembly object while the domain information (the Mesh) is stored in the Mesh object. A series of threaded loops are used to run parallel calculations on the objects created and stored within the warehouses.

MOOSE's pluggable systems are documented on the MOOSE website, and those for the Stochastic Tools module are on this webpage, accessible through the high-level system links above. Each of these systems has a set of defined polymorphic interfaces and are designed to accomplish a specific task within the simulation. The design of these systems is solid and is managed through agile methods and ticket request system on the MOOSE GitHub repository.

Data Design and Control

At a high level, the system is designed to process Hierarchical Input Text (HIT) input files to construct several objects that will constitute an finite element (FE) simulation. Some of the objects in the simulation may in turn load other file-based resources to complete the simulation. Examples include meshes or data files. The system will then assemble systems of equations and solve them using the libraries of the Code Platform. The system can then output the solution in one or more supported output formats commonly used for visualization.

Human-Machine Interface Design

The Stochastic Tools module is a command-line driven program. All interaction with the Stochastic Tools module is ultimately done through the command line. This is typical for HPC applications that use the MPI interface for running on super computing clusters. Optional GUIs may be used to assist in creating input files and launching executables on the command line.

System Design Interface

All external system interaction is performed either through file Input/Output (I/O) or through local API calls. Neither the Stochastic Tools module, nor the MOOSE framework, nor the other MOOSE modules are designed to interact with any external system directly through remote procedure calls. Any code to code coupling performed using the framework are done directly through API calls either in a static binary or after loading shared libraries.

Security Structure

The Stochastic Tools module does not require any elevated privileges to operate and does not run any stateful services, daemons or other network programs. Distributed runs rely on the MPI library.

Requirements Cross-Reference

stochastic_tools: ParameterStudy
10.1.1
The system shall be able to setup a simple parameter study with the following sampling schemes
1. Monte Carlo;
2. latin hypercube;
3. cartesian product;
4. CSV;
5. CSV when specifiying column indices;
6. CSV when specifiying column names;
7. input matrix;
Specification(s): sampling/monte_carlo, sampling/lhs, sampling/cartesian_product, sampling/csv, sampling/csv_indices, sampling/csv_names, sampling/input_matrix
Design: ParameterStudy
Issue(s): #22512
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.1.2
The system shall throw an error if the following sampler parameters are missing or unused when doing a simple parameter study
1. 'num_samples' parameter for Monte Carlo sampling;
2. 'distributions' parameter for Monte Carlo sampling;
3. 'linear_space_items' parameter with Monte Carlo sampling;
4. 'num_samples' parameter for latin hypercube sampling;
5. 'distributions' parameter for latin hypercube sampling;
6. 'linear_space_items' parameter for cartesian product sampling;
7. 'samples_file' parameter for CSV sampling;
8. both 'csv_column_indices' and 'csv_column_names' parameters are specified for CSV sampling;
9. 'matrix' parameter for input matrix sampling;
Specification(s): sampling_errors/monte_carlo_num_samples, sampling_errors/monte_carlo_distributions, sampling_errors/monte_carlo_unused, sampling_errors/lhs_num_samples, sampling_errors/lhs_distributions, sampling_errors/cartesian_product, sampling_errors/csv, sampling_errors/csv_both, sampling_errors/input_matrix
Design: ParameterStudy
Issue(s): #22512
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.1.3The system shall be able to setup a Monte Carlo parameter study with uniform, normal, Weibull, lognormal, and truncated normal distributions.
Specification(s): distributions
Design: ParameterStudy
Issue(s): #22512
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.1.4
The system shall throw an error when doing a simple parameter study if distribution parameters are
1. missing;
2. inconsitent size;
3. unused;
Specification(s): distribution_errors/missing, distribution_errors/inconsistent, distribution_errors/unused
Design: ParameterStudy
Issue(s): #22512
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.1.5
The system shall be able to setup a simple parameter study with ability to
1. compute statistics;
2. not compute statistics;
3. error if there are unused statistics parameters;
Specification(s): stats/true, stats/false, stats/unused
Design: ParameterStudy
Issue(s): #22512
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): JSONDiffRunAppRunException
10.1.6
The system shall be able to output the results of a simple parameter study with
1. no output;
2. CSV output;
3. JSON output;
Specification(s): outputs/none, outputs/csv, outputs/json
Design: ParameterStudy
Issue(s): #22512
Collection(s): FUNCTIONAL
Type(s): JSONDiffRunAppCSVDiff
10.1.7
The system shall be able to run a simple parameter study in the following modes
1. one sub-app per sample;
2. batches with one sub-app per processor while re-initializing;
3. batches one sub-app per processor while restoring;
4. batches one sub-app per processor while restoring and keeping solution;
5. batches one sub-app per processor without restoring;
Specification(s): mode/normal, mode/batch_reset, mode/batch_restore, mode/batch_keep_solution, mode/batch_no_restore
Design: ParameterStudy
Issue(s): #22512
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.1.8
The system shall be able to build a simple parameter study with an optimized execution mode by determining that
1. parameters are parsed;
2. parameters are not controllable;
3. parameters are controllable in a transient problem;
4. parameters are controllable in a pseudo-transient problem;
5. parameters are controllable in a steady-state problem;
6. parameters are controllable in a eigenvalue problem;
Specification(s): auto_mode/parsed, auto_mode/not_controllable, auto_mode/transient, auto_mode/pseudo_transient, auto_mode/steady, auto_mode/eigen
Design: ParameterStudy
Issue(s): #22512
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: SurrogateModelAuxKernel
10.2.1The system shall be able to create a surrogate that can be read in by file and use in aux kernels.
Specification(s): create
Design: SurrogateModelAuxKernel
Issue(s): #23018
Collection(s): FUNCTIONAL
Type(s): CheckFiles
10.2.2
The system shall be able to assign the values of an auxiliary variable with the evaluation of a surrogate model for
1. standard field variables and
2. array field variables.
Specification(s): evaluate/var, evaluate/array_var
Design: SurrogateModelAuxKernel
Issue(s): #23018
Collection(s): FUNCTIONAL
Type(s): RunApp
10.2.3
The system shall throw an error when evaluating a surrogate for auxiliary variable assignment if
1. a parameter is undefined,
2. a scalar parameter is not found, or
3. a coupled array variable does not match the number of components.
Specification(s): errors/parameter_unspecified, errors/scalar_missing, errors/wrong_array_size
Design: SurrogateModelAuxKernel
Issue(s): #23018
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: SurrogateModelScalarAux
10.2.4The system shall be able to evaluate a surrogate at given scalar variable values and insert the result into a scalar variable
Specification(s): scalar_surrogate_aux
Design: SurrogateModelScalarAux
Issue(s): #22980
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.2.5The system shall be able to evaluate a surrogate at given postprocessor values and insert the result into a scalar variable
Specification(s): scalar_surrogate_aux_pp_arg
Design: SurrogateModelScalarAux
Issue(s): #22980
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: LibtorchDRLControl
10.3.1The system shall be able to read a neural network from a parameter file and use it to control a transient process.
Specification(s): read-parameters
Design: LibtorchDRLControl
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.3.2The system shall be able to run with a DRL controller without having a neural net initialized in it.
Specification(s): without-nn
Design: LibtorchDRLControl
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: Uniform
10.4.1
The system shall provide distribution function including
1. uniform,
2. Weibull (3 parameter),
3. Kernel Density 1D with a Gaussian kernel and data file as input,
4. Kernel Density 1D with a Uniform kernel and data file as input,
5. Kernel Density 1D with a Gaussian kernel and data vector as input,
6. Kernel Density 1D with a Gaussian kernel and user defined bandwidth,
7. Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,
8. normal,
9. truncated normal,
10. lognormal,
11. gamma,
12. beta,
13. F,
14. student t,
15. Johnson Special Bounded (SB), and
16. logistic distributions.
Specification(s): distributions/uniform, distributions/weibull, distributions/kernel1d_gaussian, distributions/kernel1d_uniform, distributions/kernel1d_datvec, distributions/kernel1d_userbw, distributions/kernel1d_sdbw, distributions/normal, distributions/truncated_normal, distributions/lognormal, distributions/gamma, distributions/beta, distributions/fdist, distributions/studentt, distributions/johnsonsb, distributions/logistic
Design: Uniform Weibull Normal TruncatedNormal Gamma Distribution Beta Distribution F-Distribution Student t-distribution JohnsonSB Logistic
Issue(s): #12720
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: Weibull
10.4.1
The system shall provide distribution function including
1. uniform,
2. Weibull (3 parameter),
3. Kernel Density 1D with a Gaussian kernel and data file as input,
4. Kernel Density 1D with a Uniform kernel and data file as input,
5. Kernel Density 1D with a Gaussian kernel and data vector as input,
6. Kernel Density 1D with a Gaussian kernel and user defined bandwidth,
7. Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,
8. normal,
9. truncated normal,
10. lognormal,
11. gamma,
12. beta,
13. F,
14. student t,
15. Johnson Special Bounded (SB), and
16. logistic distributions.
Specification(s): distributions/uniform, distributions/weibull, distributions/kernel1d_gaussian, distributions/kernel1d_uniform, distributions/kernel1d_datvec, distributions/kernel1d_userbw, distributions/kernel1d_sdbw, distributions/normal, distributions/truncated_normal, distributions/lognormal, distributions/gamma, distributions/beta, distributions/fdist, distributions/studentt, distributions/johnsonsb, distributions/logistic
Design: Uniform Weibull Normal TruncatedNormal Gamma Distribution Beta Distribution F-Distribution Student t-distribution JohnsonSB Logistic
Issue(s): #12720
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: Normal
10.4.1
The system shall provide distribution function including
1. uniform,
2. Weibull (3 parameter),
3. Kernel Density 1D with a Gaussian kernel and data file as input,
4. Kernel Density 1D with a Uniform kernel and data file as input,
5. Kernel Density 1D with a Gaussian kernel and data vector as input,
6. Kernel Density 1D with a Gaussian kernel and user defined bandwidth,
7. Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,
8. normal,
9. truncated normal,
10. lognormal,
11. gamma,
12. beta,
13. F,
14. student t,
15. Johnson Special Bounded (SB), and
16. logistic distributions.
Specification(s): distributions/uniform, distributions/weibull, distributions/kernel1d_gaussian, distributions/kernel1d_uniform, distributions/kernel1d_datvec, distributions/kernel1d_userbw, distributions/kernel1d_sdbw, distributions/normal, distributions/truncated_normal, distributions/lognormal, distributions/gamma, distributions/beta, distributions/fdist, distributions/studentt, distributions/johnsonsb, distributions/logistic
Design: Uniform Weibull Normal TruncatedNormal Gamma Distribution Beta Distribution F-Distribution Student t-distribution JohnsonSB Logistic
Issue(s): #12720
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.4.2The system shall provide a normal distribution with the ability to directly call methods with distribution inputs.
Specification(s): normal_direct
Design: Normal
Issue(s): #12720
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.4.3The system shall produce an error if a distribution is retrieved with the differing type than supplied.
Specification(s): normal_direct_type_error
Design: Normal
Issue(s): #12720
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: TruncatedNormal
10.4.1
The system shall provide distribution function including
1. uniform,
2. Weibull (3 parameter),
3. Kernel Density 1D with a Gaussian kernel and data file as input,
4. Kernel Density 1D with a Uniform kernel and data file as input,
5. Kernel Density 1D with a Gaussian kernel and data vector as input,
6. Kernel Density 1D with a Gaussian kernel and user defined bandwidth,
7. Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,
8. normal,
9. truncated normal,
10. lognormal,
11. gamma,
12. beta,
13. F,
14. student t,
15. Johnson Special Bounded (SB), and
16. logistic distributions.
Specification(s): distributions/uniform, distributions/weibull, distributions/kernel1d_gaussian, distributions/kernel1d_uniform, distributions/kernel1d_datvec, distributions/kernel1d_userbw, distributions/kernel1d_sdbw, distributions/normal, distributions/truncated_normal, distributions/lognormal, distributions/gamma, distributions/beta, distributions/fdist, distributions/studentt, distributions/johnsonsb, distributions/logistic
Design: Uniform Weibull Normal TruncatedNormal Gamma Distribution Beta Distribution F-Distribution Student t-distribution JohnsonSB Logistic
Issue(s): #12720
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: Gamma Distribution
10.4.1
The system shall provide distribution function including
1. uniform,
2. Weibull (3 parameter),
3. Kernel Density 1D with a Gaussian kernel and data file as input,
4. Kernel Density 1D with a Uniform kernel and data file as input,
5. Kernel Density 1D with a Gaussian kernel and data vector as input,
6. Kernel Density 1D with a Gaussian kernel and user defined bandwidth,
7. Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,
8. normal,
9. truncated normal,
10. lognormal,
11. gamma,
12. beta,
13. F,
14. student t,
15. Johnson Special Bounded (SB), and
16. logistic distributions.
Specification(s): distributions/uniform, distributions/weibull, distributions/kernel1d_gaussian, distributions/kernel1d_uniform, distributions/kernel1d_datvec, distributions/kernel1d_userbw, distributions/kernel1d_sdbw, distributions/normal, distributions/truncated_normal, distributions/lognormal, distributions/gamma, distributions/beta, distributions/fdist, distributions/studentt, distributions/johnsonsb, distributions/logistic
Design: Uniform Weibull Normal TruncatedNormal Gamma Distribution Beta Distribution F-Distribution Student t-distribution JohnsonSB Logistic
Issue(s): #12720
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: Beta Distribution
10.4.1
The system shall provide distribution function including
1. uniform,
2. Weibull (3 parameter),
3. Kernel Density 1D with a Gaussian kernel and data file as input,
4. Kernel Density 1D with a Uniform kernel and data file as input,
5. Kernel Density 1D with a Gaussian kernel and data vector as input,
6. Kernel Density 1D with a Gaussian kernel and user defined bandwidth,
7. Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,
8. normal,
9. truncated normal,
10. lognormal,
11. gamma,
12. beta,
13. F,
14. student t,
15. Johnson Special Bounded (SB), and
16. logistic distributions.
Specification(s): distributions/uniform, distributions/weibull, distributions/kernel1d_gaussian, distributions/kernel1d_uniform, distributions/kernel1d_datvec, distributions/kernel1d_userbw, distributions/kernel1d_sdbw, distributions/normal, distributions/truncated_normal, distributions/lognormal, distributions/gamma, distributions/beta, distributions/fdist, distributions/studentt, distributions/johnsonsb, distributions/logistic
Design: Uniform Weibull Normal TruncatedNormal Gamma Distribution Beta Distribution F-Distribution Student t-distribution JohnsonSB Logistic
Issue(s): #12720
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: F-Distribution
10.4.1
The system shall provide distribution function including
1. uniform,
2. Weibull (3 parameter),
3. Kernel Density 1D with a Gaussian kernel and data file as input,
4. Kernel Density 1D with a Uniform kernel and data file as input,
5. Kernel Density 1D with a Gaussian kernel and data vector as input,
6. Kernel Density 1D with a Gaussian kernel and user defined bandwidth,
7. Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,
8. normal,
9. truncated normal,
10. lognormal,
11. gamma,
12. beta,
13. F,
14. student t,
15. Johnson Special Bounded (SB), and
16. logistic distributions.
Specification(s): distributions/uniform, distributions/weibull, distributions/kernel1d_gaussian, distributions/kernel1d_uniform, distributions/kernel1d_datvec, distributions/kernel1d_userbw, distributions/kernel1d_sdbw, distributions/normal, distributions/truncated_normal, distributions/lognormal, distributions/gamma, distributions/beta, distributions/fdist, distributions/studentt, distributions/johnsonsb, distributions/logistic
Design: Uniform Weibull Normal TruncatedNormal Gamma Distribution Beta Distribution F-Distribution Student t-distribution JohnsonSB Logistic
Issue(s): #12720
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: Student t-distribution
10.4.1
The system shall provide distribution function including
1. uniform,
2. Weibull (3 parameter),
3. Kernel Density 1D with a Gaussian kernel and data file as input,
4. Kernel Density 1D with a Uniform kernel and data file as input,
5. Kernel Density 1D with a Gaussian kernel and data vector as input,
6. Kernel Density 1D with a Gaussian kernel and user defined bandwidth,
7. Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,
8. normal,
9. truncated normal,
10. lognormal,
11. gamma,
12. beta,
13. F,
14. student t,
15. Johnson Special Bounded (SB), and
16. logistic distributions.
Specification(s): distributions/uniform, distributions/weibull, distributions/kernel1d_gaussian, distributions/kernel1d_uniform, distributions/kernel1d_datvec, distributions/kernel1d_userbw, distributions/kernel1d_sdbw, distributions/normal, distributions/truncated_normal, distributions/lognormal, distributions/gamma, distributions/beta, distributions/fdist, distributions/studentt, distributions/johnsonsb, distributions/logistic
Design: Uniform Weibull Normal TruncatedNormal Gamma Distribution Beta Distribution F-Distribution Student t-distribution JohnsonSB Logistic
Issue(s): #12720
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: JohnsonSB
10.4.1
The system shall provide distribution function including
1. uniform,
2. Weibull (3 parameter),
3. Kernel Density 1D with a Gaussian kernel and data file as input,
4. Kernel Density 1D with a Uniform kernel and data file as input,
5. Kernel Density 1D with a Gaussian kernel and data vector as input,
6. Kernel Density 1D with a Gaussian kernel and user defined bandwidth,
7. Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,
8. normal,
9. truncated normal,
10. lognormal,
11. gamma,
12. beta,
13. F,
14. student t,
15. Johnson Special Bounded (SB), and
16. logistic distributions.
Specification(s): distributions/uniform, distributions/weibull, distributions/kernel1d_gaussian, distributions/kernel1d_uniform, distributions/kernel1d_datvec, distributions/kernel1d_userbw, distributions/kernel1d_sdbw, distributions/normal, distributions/truncated_normal, distributions/lognormal, distributions/gamma, distributions/beta, distributions/fdist, distributions/studentt, distributions/johnsonsb, distributions/logistic
Design: Uniform Weibull Normal TruncatedNormal Gamma Distribution Beta Distribution F-Distribution Student t-distribution JohnsonSB Logistic
Issue(s): #12720
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: Logistic
10.4.1
The system shall provide distribution function including
1. uniform,
2. Weibull (3 parameter),
3. Kernel Density 1D with a Gaussian kernel and data file as input,
4. Kernel Density 1D with a Uniform kernel and data file as input,
5. Kernel Density 1D with a Gaussian kernel and data vector as input,
6. Kernel Density 1D with a Gaussian kernel and user defined bandwidth,
7. Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,
8. normal,
9. truncated normal,
10. lognormal,
11. gamma,
12. beta,
13. F,
14. student t,
15. Johnson Special Bounded (SB), and
16. logistic distributions.
Specification(s): distributions/uniform, distributions/weibull, distributions/kernel1d_gaussian, distributions/kernel1d_uniform, distributions/kernel1d_datvec, distributions/kernel1d_userbw, distributions/kernel1d_sdbw, distributions/normal, distributions/truncated_normal, distributions/lognormal, distributions/gamma, distributions/beta, distributions/fdist, distributions/studentt, distributions/johnsonsb, distributions/logistic
Design: Uniform Weibull Normal TruncatedNormal Gamma Distribution Beta Distribution F-Distribution Student t-distribution JohnsonSB Logistic
Issue(s): #12720
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: ScaledAbsDifferenceDRLRewardFunction
10.5.1The system shall be able to evaluate a linear DRL reward function with a given, time-dependent target.
Specification(s): libtorch_drl_reward_linear
Design: ScaledAbsDifferenceDRLRewardFunction
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: RandomIC
10.6.1The system shall generate parallel agnostic random initial conditions using a distribution function.
Specification(s): generate
Design: RandomIC
Issue(s): #5567 #11901 #9710
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.6.2The system shall generate an error the random initial condition is used with both a distribution and min or max value defined.
Specification(s): test_err_distribution_and_min_max
Design: RandomIC
Issue(s): #5567 #11901 #9710
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: Gaussian
10.7.1
The system shall be be able to calculate the likelihood values when
1. using a Gaussian type with log-likelihoods requested and model/experiment values are scalars;
2. using a Gaussian type with likelihoods requested and model/experiment values are scalars;
3. using a truncated Gaussian type with likelihoods requested and model/experiment values are scalars.
4. using a Extreme Value type with likelihoods requested and model/experiment values are scalars.
Specification(s): calculate_likelihoods/loglikelihood_scalar, calculate_likelihoods/likelihood_scalar, calculate_likelihoods/truncatedgaussian, calculate_likelihoods/extremevalue
Design: Gaussian TruncatedGaussian ExtremeValue
Issue(s): #23837
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.7.2
The system shall throw an error when
1. the specified lower bound is greater than the upper bound;
Specification(s): likelihoods_errors/truncatedgaussian_bounds
Design: Gaussian TruncatedGaussian ExtremeValue
Issue(s): #23837
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: TruncatedGaussian
10.7.1
The system shall be be able to calculate the likelihood values when
1. using a Gaussian type with log-likelihoods requested and model/experiment values are scalars;
2. using a Gaussian type with likelihoods requested and model/experiment values are scalars;
3. using a truncated Gaussian type with likelihoods requested and model/experiment values are scalars.
4. using a Extreme Value type with likelihoods requested and model/experiment values are scalars.
Specification(s): calculate_likelihoods/loglikelihood_scalar, calculate_likelihoods/likelihood_scalar, calculate_likelihoods/truncatedgaussian, calculate_likelihoods/extremevalue
Design: Gaussian TruncatedGaussian ExtremeValue
Issue(s): #23837
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.7.2
The system shall throw an error when
1. the specified lower bound is greater than the upper bound;
Specification(s): likelihoods_errors/truncatedgaussian_bounds
Design: Gaussian TruncatedGaussian ExtremeValue
Issue(s): #23837
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: ExtremeValue
10.7.1
The system shall be be able to calculate the likelihood values when
1. using a Gaussian type with log-likelihoods requested and model/experiment values are scalars;
2. using a Gaussian type with likelihoods requested and model/experiment values are scalars;
3. using a truncated Gaussian type with likelihoods requested and model/experiment values are scalars.
4. using a Extreme Value type with likelihoods requested and model/experiment values are scalars.
Specification(s): calculate_likelihoods/loglikelihood_scalar, calculate_likelihoods/likelihood_scalar, calculate_likelihoods/truncatedgaussian, calculate_likelihoods/extremevalue
Design: Gaussian TruncatedGaussian ExtremeValue
Issue(s): #23837
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.7.2
The system shall throw an error when
1. the specified lower bound is greater than the upper bound;
Specification(s): likelihoods_errors/truncatedgaussian_bounds
Design: Gaussian TruncatedGaussian ExtremeValue
Issue(s): #23837
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: SamplerFullSolveMultiApp
10.8.1
The system shall be able to set command line parameter(s) for sub-application that executes completely from a sample distribution for
1. for a single parameter,
2. for a single parameter for a batch of sub-applications,
3. for multiple parameters, and
4. for vector parameters for a batch of sub-applications,
5. for multiple parameters for a batch of sub-applications.
6. for multiple parameters using their global column indexes for a batch of sub-applications.
Specification(s): parent/single_normal, parent/single_batch, parent/multiple_normal, parent/vector, parent/multiple_batch, parent/multiple_batch_with_index
Design: SamplerFullSolveMultiApp MultiAppSamplerControl
Issue(s): #13320
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.8.2The system shall report an error when the supplied sampler object operates in a mode that does not allow for command line arguments to be modified.
Specification(s): wrong_batch_mode
Design: SamplerFullSolveMultiApp MultiAppSamplerControl
Issue(s): #13320
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.8.3The system shall report an error when the supplied sampler does not use the correct execution flags.
Specification(s): wrong_execute_on
Design: SamplerFullSolveMultiApp MultiAppSamplerControl
Issue(s): #13320
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.8.4The system shall report an error when '[]' syntax is not used for all parameters.
Specification(s): vector_no_bracket
Design: SamplerFullSolveMultiApp MultiAppSamplerControl
Issue(s): #13320
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.8.5The system shall report an error when provided global column index is out of bound.
Specification(s): vector_out_of_bound
Design: SamplerFullSolveMultiApp MultiAppSamplerControl
Issue(s): #13320
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.8.6The system shall support pulling postprocessor data from a sub-application for each row of sampled data.
Specification(s): normal
Design: SamplerPostprocessorTransfer SamplerFullSolveMultiApp
Issue(s): #8863
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.8.7The system shall support running sub-applications in batches.
Specification(s): batch
Design: SamplerPostprocessorTransfer SamplerFullSolveMultiApp
Issue(s): #8863
Collection(s): FUNCTIONAL
Type(s): CheckFiles
10.8.8
The stochastic tools module shall support pulling postprocessor data from a single sub-application running a batch of sampled data
1. on a single processor,
2. on multiple processors, and
3. on multiple processors using in-memory backup.
Specification(s): batch_csv/n1, batch_csv/n2, batch_csv/n2_restore
Design: SamplerPostprocessorTransfer SamplerFullSolveMultiApp
Issue(s): #8863
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.8.15
The system shall be able to control whether a sub-app is run for a certain sample
1. with a parsed function dependent on time and sampler values;
2. error if running in normal mode.
Specification(s): conditional/function, conditional/error
Design: SamplerFullSolveMultiApp ConditionalSampleReporter
Issue(s): #19350
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): JSONDiffRunException
10.8.16
The system shall support the modification of the number of complete sub-application simulations performed with
1. normal execution,
2. batch execution with memory-based restoring, and
3. batch execution with reset-based restoring.
Specification(s): dynamic/normal, dynamic/batch-restore, dynamic/batch-reset
Design: Samplers System SamplerFullSolveMultiApp
Issue(s): #16842
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.8.17The system shall report an error when the size of a sampler is altered an sub-applications are progressing with time with the main application.
Specification(s): error
Design: Samplers System SamplerFullSolveMultiApp
Issue(s): #16842
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.8.20
The system shall be able to output results from nested sub-applications in stochastic simulations using
1. normal multiapp mode;
2. batch-reset multiapp mode;
3. batch-restore multiapp mode;
Specification(s): file_output/normal, file_output/batch_reset, file_output/batch_restore
Design: SamplerFullSolveMultiApp
Issue(s): #21233
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.8.21
The system shall have consistent partitioning between multiapps and sampler for full solves
1. with less processors than rows in normal mode;
2. with more processors than rows in normal mode;
3. with specified minimum processors per app in normal mode;
4. with less processors than rows in batch-reset mode;
5. with more processors than rows in batch-reset mode;
6. with specified minimum processors per app in batch-reset mode;
7. with less processors than rows in batch-restore mode;
8. with more processors than rows in batch-restore mode;
9. with specified minimum processors per app in batch-restore mode;
10. error when partitionings do not match;
Specification(s): full_solve/normal_less_procs, full_solve/normal_more_procs, full_solve/normal_min_procs, full_solve/reset_less_procs, full_solve/reset_more_procs, full_solve/reset_min_procs, full_solve/restore_less_procs, full_solve/restore_more_procs, full_solve/restore_min_procs, full_solve/error_min_procs
Design: SamplerFullSolveMultiApp
Issue(s): #15787
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunExceptionCSVDiff
10.8.23The system shall provide the ability to create a full-solve type sub-application from sampled data from distributions.
Specification(s): full_solve
Design: SamplerFullSolveMultiApp
Issue(s): #9923
Collection(s): FUNCTIONAL
Type(s): CheckFiles
10.8.26
The system shall support performing complete solves within a sub-application that include perturbed inputs that yield repeatable results
1. using normal operation;
2. using in memory backup operation;
3. using reset operation.
Specification(s): repeat/normal, repeat/batch-restore, repeat/batch-reset
Design: SamplerFullSolveMultiApp
Issue(s): #16476
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.8.27
The system shall support performing complete solves within a sub-application that include perturbed inputs that yield changing results
1. using normal operation;
2. using in memory backup operation;
3. using reset operation.
Specification(s): new/normal, new/batch-restore, new/batch-reset
Design: SamplerFullSolveMultiApp
Issue(s): #16476
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.8.28
The system shall be able to perform stochastic simulations of steady-state models while obeying unperturbed command line arguements with:
1. command line control in normal mode;
2. command line control in batch mode;
3. parameter transfer in normal mode;
4. parameter transfer in batch-reset mode;
5. parameter transfer in batch-restore mode;
Specification(s): full_solve/cmdline_normal, full_solve/cmdline_batch, full_solve/transfer_normal, full_solve/transfer_batch_reset, full_solve/transfer_batch_restore
Design: SamplerFullSolveMultiApp
Issue(s): #19105
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.10.13
The system shall support generating random samples of data
1. that remain constant in size and
2. that are dynamic in size.
Specification(s): no_change/constant, no_change/dynamic
Design: Samplers System SamplerFullSolveMultiApp
Issue(s): #16842
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: MultiAppSamplerControl
10.8.1
The system shall be able to set command line parameter(s) for sub-application that executes completely from a sample distribution for
1. for a single parameter,
2. for a single parameter for a batch of sub-applications,
3. for multiple parameters, and
4. for vector parameters for a batch of sub-applications,
5. for multiple parameters for a batch of sub-applications.
6. for multiple parameters using their global column indexes for a batch of sub-applications.
Specification(s): parent/single_normal, parent/single_batch, parent/multiple_normal, parent/vector, parent/multiple_batch, parent/multiple_batch_with_index
Design: SamplerFullSolveMultiApp MultiAppSamplerControl
Issue(s): #13320
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.8.2The system shall report an error when the supplied sampler object operates in a mode that does not allow for command line arguments to be modified.
Specification(s): wrong_batch_mode
Design: SamplerFullSolveMultiApp MultiAppSamplerControl
Issue(s): #13320
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.8.3The system shall report an error when the supplied sampler does not use the correct execution flags.
Specification(s): wrong_execute_on
Design: SamplerFullSolveMultiApp MultiAppSamplerControl
Issue(s): #13320
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.8.4The system shall report an error when '[]' syntax is not used for all parameters.
Specification(s): vector_no_bracket
Design: SamplerFullSolveMultiApp MultiAppSamplerControl
Issue(s): #13320
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.8.5The system shall report an error when provided global column index is out of bound.
Specification(s): vector_out_of_bound
Design: SamplerFullSolveMultiApp MultiAppSamplerControl
Issue(s): #13320
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.8.12
The system shall be able to set a sub-application command line parameters from a sample distribution
1. for a single parameter and
2. for a multiple parameters.
Specification(s): parent/single, parent/multiple
Design: MultiAppSamplerControl
Issue(s): #12576
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.8.13The system shall report an error when sub-apps are constructed too early thus unable to be changed by samplers.
Specification(s): wrong_multiapp_type
Design: MultiAppSamplerControl
Issue(s): #12576
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.8.14The system shall report an error when the number of samples differs from the number of command line parameters.
Specification(s): wrong_num_params
Design: MultiAppSamplerControl
Issue(s): #12576
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.8.18
The system shall be able to apply command line parameter(s) to nested sub-applications in
1. normal multiapp mode;
2. batch multiapp mode;
Specification(s): cmd_line/normal, cmd_line/batch
Design: MultiAppSamplerControl
Issue(s): #21233
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: SamplerPostprocessorTransfer
10.8.6The system shall support pulling postprocessor data from a sub-application for each row of sampled data.
Specification(s): normal
Design: SamplerPostprocessorTransfer SamplerFullSolveMultiApp
Issue(s): #8863
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.8.7The system shall support running sub-applications in batches.
Specification(s): batch
Design: SamplerPostprocessorTransfer SamplerFullSolveMultiApp
Issue(s): #8863
Collection(s): FUNCTIONAL
Type(s): CheckFiles
10.8.8
The stochastic tools module shall support pulling postprocessor data from a single sub-application running a batch of sampled data
1. on a single processor,
2. on multiple processors, and
3. on multiple processors using in-memory backup.
Specification(s): batch_csv/n1, batch_csv/n2, batch_csv/n2_restore
Design: SamplerPostprocessorTransfer SamplerFullSolveMultiApp
Issue(s): #8863
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.12.11The system shall support the ability to transfer a single value from each sub-application for a set of stochastic data.
Specification(s): sobol_from_multiapp
Design: SamplerPostprocessorTransfer
Issue(s): #8065
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.12.12The system shall report an error if the supplied name is invalid when attempting to transfer a single value from a sub-application.
Specification(s): invalid_sub_pp_name
Design: SamplerPostprocessorTransfer
Issue(s): #8065
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.12.13
When sub-application solve does not converge, the system shall either
1. abort run,
2. transfer last computed postprocessor value,
3. or transfer NaN.
Specification(s): solve_not_converge/abort, solve_not_converge/last_computed, solve_not_converge/nan
Design: SamplerPostprocessorTransfer
Issue(s): #8065
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunExceptionCheckFilesCSVDiff

stochastic_tools: SamplerTransientMultiApp
10.8.9
The system shall support running sub-applications with input parameters varying at each time step
1. with individual sub-applications,
2. with sub-applications batches using in-memory restore functionality.
Specification(s): transient/normal, transient/batch
Design: SamplerTransientMultiApp
Issue(s): #13320
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.8.10The SamplerTransientMultiApp object shall error if the 'batch-reset' mode is supplied.
Specification(s): mode_error
Design: SamplerTransientMultiApp
Issue(s): #13320
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.8.11
The system shall support outputting results from sub-applications with input parameters varying at each time step using
1. individual sub-applications;
2. sub-application batches;
Specification(s): file_output/normal, file_output/batch
Design: SamplerTransientMultiApp
Issue(s): #13320
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.8.22
The system shall have consistent partitioning between multiapps and sampler for transient solves
1. with less processors than rows in normal mode;
2. with more processors than rows in normal mode;
3. with specified minimum processors per app in normal mode;
4. with less processors than rows in batch-restore mode;
5. with more processors than rows in batch-restore mode;
6. with specified minimum processors per app in batch-restore mode;
7. error when partitionings do not match;
Specification(s): transient/normal_less_procs, transient/normal_more_procs, transient/normal_min_procs, transient/restore_less_procs, transient/restore_more_procs, transient/restore_min_procs, transient/error_min_procs
Design: SamplerTransientMultiApp
Issue(s): #15787
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunExceptionCSVDiff
10.8.24The system shall provide the ability to create a transient sub-application from the sample data generated from distributions.
Specification(s): transient
Design: SamplerTransientMultiApp
Issue(s): #9923
Collection(s): FUNCTIONAL
Type(s): CheckFiles
10.8.25The system shall provide the ability to set a transient sub-application command line parameters from a sample distribution.
Specification(s): transient_cmdline_control
Design: SamplerTransientMultiApp
Issue(s): #17486 #17548
Collection(s): FUNCTIONAL
Type(s): Exodiff
10.8.29
The system shall be able to perform stochastic simulations of transient models while obeying unperturbed command line arguements with:
1. command line control;
2. parameter transfer in normal mode;
3. parameter transfer in batch mode;
Specification(s): transient/cmdline, transient/transfer_normal, transient/transfer_batch
Design: SamplerTransientMultiApp
Issue(s): #19105
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.12.16The system shall produce an error if the sampler sub-application does not contain a Control object with the name 'stochastic'.
Specification(s): control_missing
Design: SamplerTransientMultiApp
Issue(s): #11363
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: ConditionalSampleReporter
10.8.15
The system shall be able to control whether a sub-app is run for a certain sample
1. with a parsed function dependent on time and sampler values;
2. error if running in normal mode.
Specification(s): conditional/function, conditional/error
Design: SamplerFullSolveMultiApp ConditionalSampleReporter
Issue(s): #19350
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): JSONDiffRunException

stochastic_tools: Samplers System
10.8.16
The system shall support the modification of the number of complete sub-application simulations performed with
1. normal execution,
2. batch execution with memory-based restoring, and
3. batch execution with reset-based restoring.
Specification(s): dynamic/normal, dynamic/batch-restore, dynamic/batch-reset
Design: Samplers System SamplerFullSolveMultiApp
Issue(s): #16842
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.8.17The system shall report an error when the size of a sampler is altered an sub-applications are progressing with time with the main application.
Specification(s): error
Design: Samplers System SamplerFullSolveMultiApp
Issue(s): #16842
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.10.13
The system shall support generating random samples of data
1. that remain constant in size and
2. that are dynamic in size.
Specification(s): no_change/constant, no_change/dynamic
Design: Samplers System SamplerFullSolveMultiApp
Issue(s): #16842
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: SamplerParameterTransfer
10.8.19
The system shall be able to transfer parameter(s) to nested sub-applications in
1. normal multiapp mode;
2. batch-reset multiapp mode;
3. batch-restore multiapp mode;
Specification(s): parameter_transfer/normal, parameter_transfer/batch_reset, parameter_transfer/batch_restore
Design: SamplerParameterTransfer
Issue(s): #21233
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.12.1
The system shall include the ability to modify parameters for sub-applications using values from a distribution
1. on a single processor,
2. on multiple processors,
3. and on more processors than samples.
Specification(s): normal/n1, normal/n2, normal/n3
Design: SamplerParameterTransfer
Issue(s): #8863
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.12.2
The system shall include the ability to modify parameters for sub-applications executed in batches using values from a distribution
1. on a single processor,
2. on multiple processors, and
3. on multiple processors using in-memory sub-application restore.
Specification(s): batch/n1, batch/n2, batch/n2_restore
Design: SamplerParameterTransfer
Issue(s): #8863
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.12.3The system shall include the ability to transfer stochastic results for two sub apps.
Specification(s): batch_two_subapps
Design: SamplerParameterTransfer
Issue(s): #17079
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.12.4The 'StochasticToolsTransfer object shall error if the 'execute_on' parameter is defined when the corresponding MultiApp object is running in batch mode.
Specification(s): StochasticToolsTransfer_execute_on_error
Design: SamplerParameterTransfer
Issue(s): #8863
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.12.5The 'StochasticToolsTransfer' object shall error if the 'execute_on' parameter does not match the corresponding MultiApp object is running in normal mode.
Specification(s): StochasticToolsTransfer_execute_on_check
Design: SamplerParameterTransfer
Issue(s): #8863
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.12.6The system shall report a reasonable error if parameters for a trasnfer between multiapps are provided to stochastics transfer, which do not support this currently
Specification(s): direction_error
Design: SamplerParameterTransfer
Issue(s): #8863
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.12.8The system shall support the creation of a sub-application for each row of the stochastic data.
Specification(s): monte_carlo
Design: MonteCarlo SamplerParameterTransfer
Issue(s): #8863
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.12.15The system shall produce an error if neither a 'SamplerTransientMultiApp' nor SamplerFullSolveMultiApp is provided in SamplerParameterTransfer.
Specification(s): multiapp_type
Design: SamplerParameterTransfer
Issue(s): #11363
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.12.17The system shall produce an error if supplied vector of real values is not sized correctly within the SamplerParameterTransfer object.
Specification(s): num_parameters_wrong
Design: SamplerParameterTransfer
Issue(s): #11363
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.12.18The system shall produce an error if a vector of values is supplied to a scalar parameter.
Specification(s): not_vector
Design: SamplerParameterTransfer
Issue(s): #11363
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.12.19The system shall produce an error if sampling method differs between the sub-application and the associated sub-application data transfer.
Specification(s): sampler_mismatch
Design: SamplerParameterTransfer
Issue(s): #11363
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.12.20
The system shall be capable of transferring scalar data to sub-applications for each row of the stochastic data
1. using a Monte Carlo and
2. Sobol sampling scheme.
Specification(s): transfer/monte_carlo, transfer/sobol
Design: SamplerParameterTransfer
Issue(s): #8065
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.12.21The system shall be capable of transferring vector data to sub-applications for each row of the stochastic data.
Specification(s): monte_carlo
Design: SamplerParameterTransfer
Issue(s): #8065
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.12.22
The system shall report an error if the transferred vector to a sub-application
1. if the vector parameter does not exist;
2. if the sub-application does not consume all of the supplied data;
Specification(s): errors/invalid_name, errors/extra_data
Design: SamplerParameterTransfer
Issue(s): #8065
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.12.25The system shall support the creation of a sub-application for each row sampled data generated from a Sobol scheme.
Specification(s): sobol
Design: Sobol SamplerParameterTransfer
Issue(s): #8863
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: AISActiveLearning (AISActiveLearning)
10.9.1
The system shall be able to perform importance sampling with active learning while
1. using the U learning function.
Specification(s): sampling/ais_al_uval
Design: AISActiveLearning (AISActiveLearning)
Issue(s): #23142
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: ActiveLearningMonteCarloSampler
10.9.2
The system shall be able to perform Monte Carlo sampling with active learning with
1. a single row in the sampler using the U learning function;
2. multiple rows in the sampler using the U learning function and automatically increase the number of steps when re-training is needed;
3. a single row in the sampler using the U learning function and automatically increase the number of steps when re-training is needed;
4. a single row in the sampler using the COV learning function and automatically increase the number of steps when re-training is needed;
Specification(s): sampling/SingleProc_SingleRow_Ufunction, sampling/MultipleProc_MultipleRow_Ufunction, sampling/SingleProc_SingleRow_Ufunction_retraining, sampling/COV_squared_exp
Design: ActiveLearningMonteCarloSampler ActiveLearningGPDecision ActiveLearningGaussianProcess
Issue(s): #22280 #23292
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: ActiveLearningGPDecision
10.9.2
The system shall be able to perform Monte Carlo sampling with active learning with
1. a single row in the sampler using the U learning function;
2. multiple rows in the sampler using the U learning function and automatically increase the number of steps when re-training is needed;
3. a single row in the sampler using the U learning function and automatically increase the number of steps when re-training is needed;
4. a single row in the sampler using the COV learning function and automatically increase the number of steps when re-training is needed;
Specification(s): sampling/SingleProc_SingleRow_Ufunction, sampling/MultipleProc_MultipleRow_Ufunction, sampling/SingleProc_SingleRow_Ufunction_retraining, sampling/COV_squared_exp
Design: ActiveLearningMonteCarloSampler ActiveLearningGPDecision ActiveLearningGaussianProcess
Issue(s): #22280 #23292
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: ActiveLearningGaussianProcess
10.9.2
The system shall be able to perform Monte Carlo sampling with active learning with
1. a single row in the sampler using the U learning function;
2. multiple rows in the sampler using the U learning function and automatically increase the number of steps when re-training is needed;
3. a single row in the sampler using the U learning function and automatically increase the number of steps when re-training is needed;
4. a single row in the sampler using the COV learning function and automatically increase the number of steps when re-training is needed;
Specification(s): sampling/SingleProc_SingleRow_Ufunction, sampling/MultipleProc_MultipleRow_Ufunction, sampling/SingleProc_SingleRow_Ufunction_retraining, sampling/COV_squared_exp
Design: ActiveLearningMonteCarloSampler ActiveLearningGPDecision ActiveLearningGaussianProcess
Issue(s): #22280 #23292
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: BiFidelityActiveLearningGPDecision
10.9.3
The system shall be able to perform Monte Carlo sampling with bi-fidelity active learning with
1. a single row in the sampler using the U learning function;
2. multiple rows in the sampler using the U learning function;
Specification(s): sampling_bf/SingleProc_SingleRow_Ufunction, sampling_bf/MultipleProc_MultipleRow_Ufunction
Design: BiFidelityActiveLearningGPDecision
Issue(s): #23315
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: StatisticsReporter
10.9.4
The system shall support the calculation of statistics using
1. vectors of data from the postprocessing system and include
2. confidence level intervals for statistics calculations.
Specification(s): vpp/statistics, vpp/ci
Design: StatisticsReporter
Issue(s): #16584
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.9.5
The system shall support the calculation of statistics using
1. vector of data from the reporting system; including
2. confidence level intervals for statistics calculations
3. and error if the supplied type is not supported.
Specification(s): reporters/statistics, reporters/ci, reporters/error
Design: StatisticsReporter
Issue(s): #16584
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): JSONDiffRunException
10.9.6
The system shall support computing bias corrected and accelerated confidence level intervals of statistics
1. of a vector of data
2. of a vector of vector data
3. using data that is replicated and
4. using data that is distributed.
Specification(s): bca/basic, bca/vector, bca/replicated, bca/distributed
Design: StatisticsReporter
Issue(s): #14409 #16584
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.9.7
The system shall report an error when computing confidence level intervals when
1. the confidence level intervals are omitted;
2. the confidence level intervals are less than or equal to zero;
3. the confidence level intervals are greater than or equal to one;
4. input is not provided.
Specification(s): errors/no_ci_levels, errors/ci_levels_zero, errors/ci_levels_one, errors/no_input
Design: StatisticsReporter
Issue(s): #14409 #16584
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.9.8
The system shall support computing percentile confidence level intervals of statistics
1. of a vector of data
2. of a vector of vector data
3. using data that is replicated or
4. distributed across processors.
Specification(s): percentile/basic, percentile/vector, percentile/replicated, percentile/distributed
Design: StatisticsReporter
Issue(s): #14409 #16584
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.9.19
The system shall be capable of computing the statistics of a data vector that
1. is replicated and
2. distributed.
Specification(s): tests/replicated, tests/distributed
Design: StatisticsReporter
Issue(s): #11217 #16584
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.9.20The system shall be capable of computing the statistics from vector reporter values.
Specification(s): reporter
Design: StatisticsReporter
Issue(s): #11217 #16584
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: DirectPerturbationReporter
10.9.9
The system shall support the ability to compute direct perturbation-based sensitivity values with central differencing
1. on distributed data sets;
2. on root processor;
Specification(s): central_difference/distributed, central_difference/root
Design: DirectPerturbationReporter
Issue(s): #28671 #28799
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.9.10
The system shall support the ability to compute direct perturbation-based sensitivity values with forward differencing
1. on distributed data sets;
2. on root processor;
Specification(s): forward_difference/distributed, forward_difference/root
Design: DirectPerturbationReporter
Issue(s): #28671 #28799
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.9.11The system shall support the ability to compute direct perturbation-based relative sensitivity values.
Specification(s): relative_sensitivity
Design: DirectPerturbationReporter
Issue(s): #28671 #28799
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: MappingReporter
10.9.12The system should be able to map the solutions in a snapshot container into a latent space.
Specification(s): from_parallelstorage
Design: MappingReporter
Issue(s): #23619
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.9.13The system should be able to extract and map variables in nonlinear system into a latent (low-dimensional) space.
Specification(s): from_solutions
Design: MappingReporter
Issue(s): #23619
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: MorrisReporter
10.9.14
The system shall support the ability to compute Morris sensitivity values with
1. distributed data;
2. replicated data;
Specification(s): morris/distributed, morris/replicated
Design: MorrisReporter
Issue(s): #21191
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.9.15
The system shall support the ability to compute Morris sensitivity values for vector-type data that is
1. distributed;
2. on root processor;
Specification(s): morris_vec/distributed, morris_vec/root
Design: MorrisReporter
Issue(s): #21191
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: ParallelSolutionStorage
10.9.16The system should be able to print serialized snapshots in a distributed fashion in a json format.
Specification(s): print-p2
Design: ParallelSolutionStorage
Issue(s): #23619
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: SobolReporter
10.9.17The system shall support the ability to compute first, second, and total-effect Sobol sensitivity indices with a reporter.
Specification(s): sobol
Design: SobolReporter
Issue(s): #15558
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.9.18The system shall support the ability to compute Sobol sensitivity indices for vector-type data.
Specification(s): sobol_vec
Design: SobolReporter
Issue(s): #15558
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: StochasticMatrix
10.9.21
The system shall be able to ouptut samples from a sampler with
1. serialized output;
2. distributed output;
3. specified column names;
Specification(s): sample_only/serial, sample_only/parallel, sample_only/names
Design: StochasticMatrix
Issue(s): #22827
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.9.22The system shall be able to output samples from a sampler along side stochastic simulation results.
Specification(s): simulation
Design: StochasticMatrix
Issue(s): #22827
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.9.23
The system shall throw an error when attempting to output samples from a sampler when
1. inputting the wrong number of column names;
2. using a different sampler than the transfer's;
Specification(s): errors/wrong_num_names, errors/wrong_sampler
Design: StochasticMatrix
Issue(s): #22827
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: StochasticReporter
10.9.24
The system shall support the ability to use transferred reporter data to
1. compute statistics.
Specification(s): sr/stats
Design: StochasticReporter
Issue(s): #16166
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: Adaptive Importance Sampling (AIS)
10.10.1The system shall include an Adaptive Importance Sampling method for sampling distribution data.
Specification(s): ais
Design: Adaptive Importance Sampling (AIS)
Issue(s): #17664
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.10.2
The system shall throw an error when
1. the selected sampler type is not of an adaptive type when performing adaptive decision;
Specification(s): ais_errors/ais_error1
Design: Adaptive Importance Sampling (AIS)
Issue(s): #17664
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: Parallel Subset Simulation (PSS)
10.10.3The system shall include an Parallel Subset Simulation method for sampling distribution data.
Specification(s): pss1
Design: Parallel Subset Simulation (PSS)
Issue(s): #19398
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.10.4
The system shall throw an error when
1. the selected sampler type is not of an adaptive type.
Specification(s): pss_errors/pss_error1
Design: Parallel Subset Simulation (PSS)
Issue(s): #19398
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: Cartesian1D
10.10.5The system shall include the ability to create a 1D Cartesian sampling scheme.
Specification(s): test
Design: Cartesian1D
Issue(s): #24566
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.10.6
The system shall throw an error when performing 1D Cartesian sampling if
1. the specified grid does not form a triplet,
2. the number of nominal values specified does not match the number of sample columns,
3. the number of grid points is not an integer, or
4. the number of grid points is negative.
Specification(s): errors/no_triplet, errors/wrong_num_values, errors/non_int, errors/negative
Design: Cartesian1D
Issue(s): #24566
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: CartesianProductSampler
10.10.7The system shall include the ability to create a Cartesian product sampling scheme.
Specification(s): test
Design: CartesianProductSampler
Issue(s): #14457
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: CSVSampler
10.10.8
The CSV Sampler shall read samples from a CSV file while the sample data is
1. distributed across processors,
2. replicated across processors, and
3. distributed across processors with the output also distributed.
Specification(s): csv_sampler/sampling_test, csv_sampler/sampling_test_global, csv_sampler/sampling_test_distributed
Design: CSVSampler
Issue(s): #16462
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.10.9The CSV Sampler shall sample from a CSV file when column indices are provided.
Specification(s): csv_sampler_indices
Design: CSVSampler
Issue(s): #16462
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.10.10The CSV Sampler shall sample from a CSV file when column names are provided.
Specification(s): csv_sampler_names
Design: CSVSampler
Issue(s): #16462
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: DirectPerturbationSampler
10.10.11
The system shall be able to generate samples for a direct perturbation method with a central difference scheme:
1. with replicated output;
2. with parallel output;
Specification(s): central_difference/serial, central_difference/parallel
Design: DirectPerturbationSampler
Issue(s): #28671
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.10.12
The system shall be able to generate samples for a direct perturbation method with a forward difference scheme:
1. with replicated output;
2. with parallel output;
Specification(s): forward_difference/serial, forward_difference/parallel
Design: DirectPerturbationSampler
Issue(s): #28671
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: Sampler
10.10.14The system shall support the creation of data sampled from distribution during the initial setup of a simulation.
Specification(s): initial
Design: Sampler
Issue(s): #8065
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: InputMatrix
10.10.15The system shall be able to sample an input-defined matrix.
Specification(s): sampling_test
Design: InputMatrix
Issue(s): #22512
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: LatinHypercube
10.10.16
The system shall support the ability to sample data using the Latin Hypercube method that can operate
1. using global matrix,
2. a local matrix,
3. or row-by-row.
Specification(s): modes/global, modes/local, modes/row
Design: LatinHypercube
Issue(s): #14830
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.10.17
The system shall support the ability to sample data using the Latin Hypercube method with more processors than rows that can operate
1. using global matrix,
2. a local matrix,
3. or row-by-row.
Specification(s): more_procs/global, more_procs/local, more_procs/row
Design: LatinHypercube
Issue(s): #14830
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.10.18The system shall include a utility that visually displays results of plotting Latin Hypercube test.
Specification(s): visualize
Design: LatinHypercube
Issue(s): #14830
Collection(s): FUNCTIONAL
Type(s): CheckFiles

stochastic_tools: PMCMCBase (PMCMCBase)
10.10.19
The system shall be be able to perform MCMC sampling when
1. using the base MCMC class;
2. using the base MCMC class with bounds specified;
3. using the independent Metropolis-Hastings class;
4. using the differential evolution sampler class;
5. using the stretch sampler class;
6. using the differential evolution sampler class with variance inferred;
Specification(s): mcmc_sampling/pmcmc_base, mcmc_sampling/pmcmc_base_bounds, mcmc_sampling/imh_5prop, mcmc_sampling/des_5prop, mcmc_sampling/ss_5prop, mcmc_sampling/des_5prop_var
Design: PMCMCBase (PMCMCBase)
Issue(s): #23837
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.10.20
The system shall throw an error when
1. only the lower bounds specified;
2. the specified bounds are not of the same size;
3. the priors, bounds, and initial values are not of the same size;
4. the proposal stds, initial values, and priors are not of the same size;
5. the number of scales provided do not match the number of tunable params;
Specification(s): mcmc_sampling_errors/pmcmc_base_bounds1, mcmc_sampling_errors/pmcmc_base_bounds2, mcmc_sampling_errors/pmcmc_base_sizes, mcmc_sampling_errors/imh_std_sizes, mcmc_sampling_errors/des_scale_sizes
Design: PMCMCBase (PMCMCBase)
Issue(s): #23837
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: MonteCarlo
10.10.21
The system shall include a Monte Carlo method for sampling distribution data including
1. a uniform distribution distributed across processors,
2. a uniform distribution replicated across processors,
3. a uniform distribution distributed across processors (output is also distributed),
4. a Wiebull distribution distributed across processors, and
5. a Weibull distribution replicated across processors.
Specification(s): monte_carlo/uniform, monte_carlo/uniform_global, monte_carlo/uniform_distributed, monte_carlo/weibull, monte_carlo/weibull_global
Design: MonteCarlo
Issue(s): #8065
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.12.8The system shall support the creation of a sub-application for each row of the stochastic data.
Specification(s): monte_carlo
Design: MonteCarlo SamplerParameterTransfer
Issue(s): #8863
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: Morris
10.10.22
The system shall include a Morris trajectory method for sampling distribution data:
1. with replicated output;
2. with parallel output;
3. with more processors than trajectories;
Specification(s): morris/serial, morris/parallel, morris/parallel_more
Design: Morris
Issue(s): #21191
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: NestedMonteCarlo
10.10.23
The system shall include a nested Monte Carlo sampling scheme where sets of distributions are sampled as nested loops of rows
1. in serial;
2. in parallel;
Specification(s): nested_monte_carlo/serial, nested_monte_carlo/parallel
Design: NestedMonteCarlo
Issue(s): #20130
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.10.24The system shall report an error when the number of nested Monte Carlo loops does not match the number of sets of distributions.
Specification(s): error
Design: NestedMonteCarlo
Issue(s): #20130
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: Sobol
10.10.25
The system shall include a SOBOL method for sampling distribution data:
1. with the re-sampling matrix and
2. without the re-sampling matrix.
Specification(s): sobol/resample, sobol/no_resample
Design: Sobol
Issue(s): #8065
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.10.26
The system shall report an error if the SOBOL sampling method is setup with input sampling matrices
1. with differing number of rows;
2. with differing number of columns; and
3. if the matrices are the same.
Specification(s): errors/row_mismatch, errors/col_mismatch, errors/same_matrix
Design: Sobol
Issue(s): #8065
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.12.25The system shall support the creation of a sub-application for each row sampled data generated from a Sobol scheme.
Specification(s): sobol
Design: Sobol SamplerParameterTransfer
Issue(s): #8863
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: VectorPostprocessorSampler
10.10.27The system shall be able to perform stochastic sampling based on global vector data.
Specification(s): VectorPostprocessors_sampler
Design: VectorPostprocessorSampler
Issue(s): #24651
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: Surrogates System
10.11.1
The system shall demonstrate cross validation
1. for a 1-dimensional polynomial regression surrogate with Real
2. and std::vector<Real> response types.
3. and throw an error if a SurrogateModel is not provided.
Specification(s): cross_validate/cv_real, cross_validate/cv_vector_real, cross_validate/surr-error
Design: Surrogates System
Issue(s): #21399
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): JSONDiffRunException
10.11.13
The system shall support the creation of surrogate models that can be
1. trained with replicated stochastic data and
2. evaluated separately (with replicated data);
3. trained with distributed stochastic data and
4. evaluated separately (with distributed data);
5. trained with distributed stochastic data;
6. evaluated separately with a different number of processors;
7. and be trained and evaluated in memory with a single input file.
Specification(s): surrogate/train, surrogate/evaluate, surrogate/train_distributed, surrogate/evaluate_distributed, surrogate/train_three_ranks, surrogate/evaluate_two_ranks, surrogate/evaluate_and_train
Design: Surrogates System
Issue(s): #14875
Collection(s): FUNCTIONAL
Type(s): JSONDiffCheckFiles

stochastic_tools: GaussianProcessTrainer
10.11.2
The system shall demonstrate a gaussian process surrogate by
1. training a Gaussian process model and
2. evaluating the trained Gaussian process model
Specification(s): store_load/GP_training, store_load/GP_testing
Design: GaussianProcessTrainer GaussianProcess
Issue(s): #15482
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff
10.11.3
The system shall be able to produce a Gaussian process surrogate with
1. a squared exponential kernel;
2. an exponential kernel;
3. Matern half integer kernel;
Specification(s): kernels/GP_squared_exponential, kernels/GP_exponential, kernels/GP_Matern_half_int
Design: GaussianProcessTrainer GaussianProcess
Issue(s): #15482
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.11.4
The system shall be be able to tune hyperparameters of a Gaussian process surrogate with
1. a squared exponential kernel using Adam;
2. an exponential kernel using Adam;
3. a Matern half integer kernel using Adam;
4. a Matern half integer kernel using Adam with mini-batch sampling;
Specification(s): tuning/GP_squared_exponential_tuned_adam, tuning/GP_exponential_tuned_adam, tuning/GP_Matern_half_int_tuned_adam, tuning/GP_Matern_half_int_tuned_adam_minibatch
Design: GaussianProcessTrainer GaussianProcess
Issue(s): #15482
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.11.5
The system shall throw an error when
1. the batch size is greater than the training data set size for Adam optimization.
Specification(s): errors/optimization_adam_batch_size
Design: GaussianProcessTrainer GaussianProcess
Issue(s): #15482
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.11.14
The system shall be able to train a multi-output Gaussian process using Linear Model of Coregionalization
1. without tuning the hyperparameters.
2. with tuning the hyperparameters.
3. and load the trained surrogate from a file.
Specification(s): lmc/untuned, lmc/tuned, lmc/load
Design: GaussianProcessTrainer LMC GaussianProcessSurrogate
Issue(s): #25438
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: GaussianProcess
10.11.2
The system shall demonstrate a gaussian process surrogate by
1. training a Gaussian process model and
2. evaluating the trained Gaussian process model
Specification(s): store_load/GP_training, store_load/GP_testing
Design: GaussianProcessTrainer GaussianProcess
Issue(s): #15482
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff
10.11.3
The system shall be able to produce a Gaussian process surrogate with
1. a squared exponential kernel;
2. an exponential kernel;
3. Matern half integer kernel;
Specification(s): kernels/GP_squared_exponential, kernels/GP_exponential, kernels/GP_Matern_half_int
Design: GaussianProcessTrainer GaussianProcess
Issue(s): #15482
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.11.4
The system shall be be able to tune hyperparameters of a Gaussian process surrogate with
1. a squared exponential kernel using Adam;
2. an exponential kernel using Adam;
3. a Matern half integer kernel using Adam;
4. a Matern half integer kernel using Adam with mini-batch sampling;
Specification(s): tuning/GP_squared_exponential_tuned_adam, tuning/GP_exponential_tuned_adam, tuning/GP_Matern_half_int_tuned_adam, tuning/GP_Matern_half_int_tuned_adam_minibatch
Design: GaussianProcessTrainer GaussianProcess
Issue(s): #15482
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.11.5
The system shall throw an error when
1. the batch size is greater than the training data set size for Adam optimization.
Specification(s): errors/optimization_adam_batch_size
Design: GaussianProcessTrainer GaussianProcess
Issue(s): #15482
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException

stochastic_tools: LibtorchANNTrainer
10.11.6The system shall be able to train and evaluate a libtorch-based neural network in the same input file.
Specification(s): train_and_evaluate
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.11.7The system shall be able to train a libtorch-based neural network.
Specification(s): train
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CheckFiles
10.11.8The system shall be able to evaluate a previously trained, libtorch-based neural network.
Specification(s): evaluate
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.11.9The system shall be able to retrain a pretrained and saved libtorch-based neural network.
Specification(s): retrain
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.11.10The system shall be able to train a libtorch-based neural network using a relative tolerance instead of fixed epoch number.
Specification(s): train-with-rel-tol
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CheckFiles
10.11.11The system shall be able to train a libtorch-based neural network with standardized input and output parameters.
Specification(s): train-standardized
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CheckFiles
10.11.12The system shall be able to evaluate a previously trained, libtorch-based neural network with standardized input and output parameters.
Specification(s): evaluate-standardized
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: LibtorchANNSurrogate
10.11.6The system shall be able to train and evaluate a libtorch-based neural network in the same input file.
Specification(s): train_and_evaluate
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.11.7The system shall be able to train a libtorch-based neural network.
Specification(s): train
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CheckFiles
10.11.8The system shall be able to evaluate a previously trained, libtorch-based neural network.
Specification(s): evaluate
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.11.9The system shall be able to retrain a pretrained and saved libtorch-based neural network.
Specification(s): retrain
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.11.10The system shall be able to train a libtorch-based neural network using a relative tolerance instead of fixed epoch number.
Specification(s): train-with-rel-tol
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CheckFiles
10.11.11The system shall be able to train a libtorch-based neural network with standardized input and output parameters.
Specification(s): train-standardized
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CheckFiles
10.11.12The system shall be able to evaluate a previously trained, libtorch-based neural network with standardized input and output parameters.
Specification(s): evaluate-standardized
Design: LibtorchANNTrainer LibtorchANNSurrogate
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: LMC
10.11.14
The system shall be able to train a multi-output Gaussian process using Linear Model of Coregionalization
1. without tuning the hyperparameters.
2. with tuning the hyperparameters.
3. and load the trained surrogate from a file.
Specification(s): lmc/untuned, lmc/tuned, lmc/load
Design: GaussianProcessTrainer LMC GaussianProcessSurrogate
Issue(s): #25438
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: GaussianProcessSurrogate
10.11.14
The system shall be able to train a multi-output Gaussian process using Linear Model of Coregionalization
1. without tuning the hyperparameters.
2. with tuning the hyperparameters.
3. and load the trained surrogate from a file.
Specification(s): lmc/untuned, lmc/tuned, lmc/load
Design: GaussianProcessTrainer LMC GaussianProcessSurrogate
Issue(s): #25438
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: NearestPointTrainer
10.11.15
The system shall create a surrogate that evaluates the closes point from training data by
1. training then
2. evaluating,
3. training and loading, and
4. using explictly specified predictors.
Specification(s): nearest_point/train, nearest_point/load, nearest_point/train_and_load, nearest_point/predictors
Design: NearestPointTrainer NearestPointSurrogate
Issue(s): #14933
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff
10.11.16The system shall be able to create nearest point surrogates with vector-type response.
Specification(s): vector
Design: NearestPointTrainer NearestPointSurrogate
Issue(s): #14933
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: NearestPointSurrogate
10.11.15
The system shall create a surrogate that evaluates the closes point from training data by
1. training then
2. evaluating,
3. training and loading, and
4. using explictly specified predictors.
Specification(s): nearest_point/train, nearest_point/load, nearest_point/train_and_load, nearest_point/predictors
Design: NearestPointTrainer NearestPointSurrogate
Issue(s): #14933
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff
10.11.16The system shall be able to create nearest point surrogates with vector-type response.
Specification(s): vector
Design: NearestPointTrainer NearestPointSurrogate
Issue(s): #14933
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: PODReducedBasisTrainer
10.11.17
The system shall demonstrate a POD-RB surrogate (with Dirichlet BC) by
1. training using known 4D data
2. and then evaluating new samples separately for new data.
Specification(s): pod_boundary/train, pod_boundary/load
Design: PODReducedBasisTrainer PODReducedBasisSurrogate PODFullSolveMultiApp PODSamplerSolutionTransfer PODResidualTransfer
Issue(s): #15538
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff
10.11.20
PODReducedBasisTrainer shall throw an error when
1. the variable names cannot be found on sub-applications,
2. the number of energy limits and variable names do not match,
3. the number of tag names and tag types do not match,
4. the Dirichlet tag types do not exist,
5. and the residual generation is called before having the basis vectors.
Specification(s): trainer/var_names, trainer/en_limits, trainer/tag_types, trainer/dir_tag_names, trainer/wrong_res_transfer
Design: PODReducedBasisTrainer PODResidualTransfer PODSamplerSolutionTransfer
Issue(s): #15538
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.11.22
The system shall demonstrate a POD-RB surrogate (without Dirichlet BC) by
1. training using known 3D data,
2. saving the eigenvalues,
3. then evaluating new samples separately for new data,
4. and doing both together in one input file.
Specification(s): pod_internal/train, pod_internal/save_ev, pod_internal/load, pod_internal/train_and_load
Design: PODReducedBasisTrainer PODReducedBasisSurrogate PODFullSolveMultiApp PODSamplerSolutionTransfer PODResidualTransfer
Issue(s): #15538
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff

stochastic_tools: PODReducedBasisSurrogate
10.11.17
The system shall demonstrate a POD-RB surrogate (with Dirichlet BC) by
1. training using known 4D data
2. and then evaluating new samples separately for new data.
Specification(s): pod_boundary/train, pod_boundary/load
Design: PODReducedBasisTrainer PODReducedBasisSurrogate PODFullSolveMultiApp PODSamplerSolutionTransfer PODResidualTransfer
Issue(s): #15538
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff
10.11.21
PODReducedBasisSurrogate shall throw an error when
1. the number of inputs in 'change_rank' and 'new_ranks' is not the same.
Specification(s): surrogate/var_name
Design: PODReducedBasisSurrogate
Issue(s): #15538
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.11.22
The system shall demonstrate a POD-RB surrogate (without Dirichlet BC) by
1. training using known 3D data,
2. saving the eigenvalues,
3. then evaluating new samples separately for new data,
4. and doing both together in one input file.
Specification(s): pod_internal/train, pod_internal/save_ev, pod_internal/load, pod_internal/train_and_load
Design: PODReducedBasisTrainer PODReducedBasisSurrogate PODFullSolveMultiApp PODSamplerSolutionTransfer PODResidualTransfer
Issue(s): #15538
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff

stochastic_tools: PODFullSolveMultiApp
10.11.17
The system shall demonstrate a POD-RB surrogate (with Dirichlet BC) by
1. training using known 4D data
2. and then evaluating new samples separately for new data.
Specification(s): pod_boundary/train, pod_boundary/load
Design: PODReducedBasisTrainer PODReducedBasisSurrogate PODFullSolveMultiApp PODSamplerSolutionTransfer PODResidualTransfer
Issue(s): #15538
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff
10.11.18
PODFullSolveMultiapp shall throw an error when
1. the trainer object cannot be found.
2. the trainer is run with more than one processors per subapp.
3. the number of samples is lower than the number of processors.
Specification(s): multiapp/trainer_name, multiapp/max_procs_per_app, multiapp/more_procs_than_samples
Design: PODFullSolveMultiApp
Issue(s): #15538
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.11.22
The system shall demonstrate a POD-RB surrogate (without Dirichlet BC) by
1. training using known 3D data,
2. saving the eigenvalues,
3. then evaluating new samples separately for new data,
4. and doing both together in one input file.
Specification(s): pod_internal/train, pod_internal/save_ev, pod_internal/load, pod_internal/train_and_load
Design: PODReducedBasisTrainer PODReducedBasisSurrogate PODFullSolveMultiApp PODSamplerSolutionTransfer PODResidualTransfer
Issue(s): #15538
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff

stochastic_tools: PODSamplerSolutionTransfer
10.11.17
The system shall demonstrate a POD-RB surrogate (with Dirichlet BC) by
1. training using known 4D data
2. and then evaluating new samples separately for new data.
Specification(s): pod_boundary/train, pod_boundary/load
Design: PODReducedBasisTrainer PODReducedBasisSurrogate PODFullSolveMultiApp PODSamplerSolutionTransfer PODResidualTransfer
Issue(s): #15538
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff
10.11.19
PODSamplerSolutionTransfer shall throw an error when
1. the trainer object cannot be found.
Specification(s): solution_transfer/trainer_name
Design: PODSamplerSolutionTransfer
Issue(s): #15538
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.11.20
PODReducedBasisTrainer shall throw an error when
1. the variable names cannot be found on sub-applications,
2. the number of energy limits and variable names do not match,
3. the number of tag names and tag types do not match,
4. the Dirichlet tag types do not exist,
5. and the residual generation is called before having the basis vectors.
Specification(s): trainer/var_names, trainer/en_limits, trainer/tag_types, trainer/dir_tag_names, trainer/wrong_res_transfer
Design: PODReducedBasisTrainer PODResidualTransfer PODSamplerSolutionTransfer
Issue(s): #15538
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.11.22
The system shall demonstrate a POD-RB surrogate (without Dirichlet BC) by
1. training using known 3D data,
2. saving the eigenvalues,
3. then evaluating new samples separately for new data,
4. and doing both together in one input file.
Specification(s): pod_internal/train, pod_internal/save_ev, pod_internal/load, pod_internal/train_and_load
Design: PODReducedBasisTrainer PODReducedBasisSurrogate PODFullSolveMultiApp PODSamplerSolutionTransfer PODResidualTransfer
Issue(s): #15538
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff

stochastic_tools: PODResidualTransfer
10.11.17
The system shall demonstrate a POD-RB surrogate (with Dirichlet BC) by
1. training using known 4D data
2. and then evaluating new samples separately for new data.
Specification(s): pod_boundary/train, pod_boundary/load
Design: PODReducedBasisTrainer PODReducedBasisSurrogate PODFullSolveMultiApp PODSamplerSolutionTransfer PODResidualTransfer
Issue(s): #15538
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff
10.11.20
PODReducedBasisTrainer shall throw an error when
1. the variable names cannot be found on sub-applications,
2. the number of energy limits and variable names do not match,
3. the number of tag names and tag types do not match,
4. the Dirichlet tag types do not exist,
5. and the residual generation is called before having the basis vectors.
Specification(s): trainer/var_names, trainer/en_limits, trainer/tag_types, trainer/dir_tag_names, trainer/wrong_res_transfer
Design: PODReducedBasisTrainer PODResidualTransfer PODSamplerSolutionTransfer
Issue(s): #15538
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.11.22
The system shall demonstrate a POD-RB surrogate (without Dirichlet BC) by
1. training using known 3D data,
2. saving the eigenvalues,
3. then evaluating new samples separately for new data,
4. and doing both together in one input file.
Specification(s): pod_internal/train, pod_internal/save_ev, pod_internal/load, pod_internal/train_and_load
Design: PODReducedBasisTrainer PODReducedBasisSurrogate PODFullSolveMultiApp PODSamplerSolutionTransfer PODResidualTransfer
Issue(s): #15538
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff

stochastic_tools: PolynomialChaos
10.11.23
The system shall compute polynomial chaos coefficents using
1. MonteCarlo sampler with Uniform distribution,
2. MonteCarlo sampler with Uniform distribution using least-squares regression,
3. MonteCarlo sampler with Uniform distribution using Ridge regression,
4. Quadrature sampler with Uniform distribution, and
5. Quadrature sampler with Normal distribution.
Specification(s): coefficients/monte_carlo_integration, coefficients/monte_carlo_ols, coefficients/monte_carlo_ridge, coefficients/gauss_legendre_integration, coefficients/gauss_hermite
Design: PolynomialChaos
Issue(s): #14710
Collection(s): FUNCTIONAL
Type(s): JSONDiffCSVDiff

stochastic_tools: PolynomialChaosReporter
10.11.24
The system shall compute relevant statistics with polynomial chaos expansion including
1. statistical moments with Legendre polynomials,
2. statistical moments with Hermite polynomials,
3. sampler and user defined local sensitivities with Legendre polynomials,
4. sampler and user defined local sensitivities with Hermite polynomials, and
5. Sobol sensitivity indices.
Specification(s): statistics/legendre_moment_calculation, statistics/hermite_moment_calculation, statistics/legendre_local_sensitivity, statistics/hermite_local_sensitivity, statistics/sobol
Design: PolynomialChaosReporter
Issue(s): #14836
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: QuadratureSampler
10.11.25
The system shall include the ability to use sparse grid methods to evaluate polynomial chaos expansion coefficients including
1. Smolyak and
2. Clenshaw-Curtis methods.
Specification(s): sparse/smolyak_sobol, sparse/clenshaw_curtis_sobol
Design: QuadratureSampler
Issue(s): #14833
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: PolynomialRegressionTrainer
10.11.26
The system shall throw an error when
1. the number of samples does not match the number of results.
Specification(s): errors/wrong_sampler
Design: PolynomialRegressionTrainer
Issue(s): #15361
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.11.27
The system shall demonstrate a polnomial regression surrogate by
1. training using known 3D data
2. and then evaluating new samples separately for the same data
3. and then doing both on another 1D case.
Specification(s): poly_reg/train, poly_reg/load, poly_reg/train_and_load
Design: PolynomialRegressionTrainer PolynomialRegressionSurrogate
Issue(s): #15361
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff
10.11.28The system shall be able to create polynomial regression surrogate with vector-type response.
Specification(s): vector
Design: PolynomialRegressionTrainer PolynomialRegressionSurrogate
Issue(s): #15361
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: PolynomialRegressionSurrogate
10.11.27
The system shall demonstrate a polnomial regression surrogate by
1. training using known 3D data
2. and then evaluating new samples separately for the same data
3. and then doing both on another 1D case.
Specification(s): poly_reg/train, poly_reg/load, poly_reg/train_and_load
Design: PolynomialRegressionTrainer PolynomialRegressionSurrogate
Issue(s): #15361
Collection(s): FUNCTIONAL
Type(s): CheckFilesCSVDiff
10.11.28The system shall be able to create polynomial regression surrogate with vector-type response.
Specification(s): vector
Design: PolynomialRegressionTrainer PolynomialRegressionSurrogate
Issue(s): #15361
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: LibtorchNeuralNetControlTransfer
10.12.7The system shall be able to transfer an artificial neural network between a trainer object and a control object residing on different apps.
Specification(s): torch-parameters
Design: LibtorchNeuralNetControlTransfer
Issue(s): #19571
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: StochasticResults
10.12.9The system shall produce an error if neither a 'SamplerTransientMultiApp' nor SamplerFullSolveMultiApp is provided in SamplerPostprocessorTransfer.
Specification(s): wrong_multi_app
Design: StochasticResults
Issue(s): #9419
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.12.10The system shall produce an error if the 'result' object in 'SamplerPostprocessorTransfer' is not a 'StochasticResults object'.
Specification(s): require_stochastic_results
Design: StochasticResults
Issue(s): #9419
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): RunException
10.15.9The system shall support the collection of stochastic data from multiple sub-applications.
Specification(s): multiple
Design: StochasticResults
Issue(s): #14414
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.15.17
The system shall support the collection of stochastic data that is
1. replicated on all processors and
2. distributed across many.
Specification(s): parallel_type/replicated, parallel_type/distributed
Design: StochasticResults
Issue(s): #14410
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.15.18
The system shall support the labeling of collection of stochastic data
1. with custom prefix and
2. without a prefix.
Specification(s): prefix/custom, prefix/none
Design: StochasticResults
Issue(s): #14410
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.15.19
The system shall support the collection of stochastic data that
1. can be appended into a single data set or
2. or contain a single file per timestep.
Specification(s): data/complete, data/time
Design: StochasticResults
Issue(s): #14412
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: SamplerReporterTransfer
10.12.14
The system shall support the ability to transfer reporter data from each sub-application for a set of stochastic data
1. in normal mode,
2. in batch mode,
3. with distributed output,
4. with more processors than samples,
5. and error if transferring unsupported type.
Specification(s): transfer/normal, transfer/batch, transfer/distributed, transfer/more_cpus, transfer/invalid_type
Design: SamplerReporterTransfer
Issue(s): #16166
Collection(s): FUNCTIONALFAILURE_ANALYSIS
Type(s): JSONDiffRunException

stochastic_tools: SerializedSolutionTransfer
10.12.23
The system shall be able to serialize solution samples on the root process of each subapplication and transfer results to a parallel storage in
1. batch-restore mode,
2. batch-reset mode, and
3. normal mode.
Specification(s): transfer_on_roots/batch_restore, transfer_on_roots/batch_reset, transfer_on_roots/normal
Design: SerializedSolutionTransfer
Issue(s): #23619
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.12.24
The system shall be able to distribute the solution samples among the processors of each subapplication, serialize and transfer them to a parallel storage on the corresponding ranks in
1. batch-restore mode,
2. batch-reset mode, and
3. normal mode.
Specification(s): transfer_fully_distributed/batch_restore, transfer_fully_distributed/batch_reset, transfer_fully_distributed/normal
Design: SerializedSolutionTransfer
Issue(s): #23619
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: InverseMapping
10.13.1The system shall be able to create a surrogate model for the solution coordinates in the latent space.
Specification(s): create_surrogate
Design: InverseMapping
Issue(s): #23619
Collection(s): FUNCTIONAL
Type(s): CheckFiles
10.13.2
The system should be able to reconstruct a full solution field for a given variable based on
1. coordinates determined by a surrogate model.
2. manually specified coordinates.
Specification(s): inverse_map/with_surrogate, inverse_map/with_manual_input
Design: InverseMapping
Issue(s): #23619
Collection(s): FUNCTIONAL
Type(s): Exodiff
10.13.3
The system shall be bale to reconstruct linearly parameterized functions exactly with a few spatial modes using
1. polynomial regression surrogates for the expansion coefficients.
Specification(s): inverse_map_verification/with_polyreg_csv
Design: InverseMapping
Issue(s): #23619
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: PODMapping
10.14.1The system should be able to build a variable mapping by computing the SVD of a snapshot matrix in serial.
Specification(s): svd_1_proc_per_app
Design: PODMapping
Issue(s): #23619
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.14.2The system should be able to build a variable mapping by computing the SVD of a snapshot matrix in parallel.
Specification(s): svd_2_proc_per_app
Design: PODMapping
Issue(s): #23619
Collection(s): FUNCTIONAL
Type(s): JSONDiff
10.14.3The system should be able to build a variable mapping by computing the SVD of a snapshot matrix in parallel with having data on only root processors.
Specification(s): svd_2_proc_per_app_root
Design: PODMapping
Issue(s): #23619
Collection(s): FUNCTIONAL
Type(s): JSONDiff

stochastic_tools: SamplerData
10.15.10
The system shall be able to ouptut samples from a sampler using the sampling method
1. get global matrix;
2. get local matrix;
3. get next local row;
Specification(s): get_sample_types/get_global_samples, get_sample_types/get_local_samples, get_sample_types/get_next_local_row
Design: SamplerData
Issue(s): #9402
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.15.11
The system shall be able to ouptut distributed samples from a sampler using the sampling method
1. get local matrix;
2. get next local row;
Specification(s): parallel/get_local_samples, parallel/get_next_local_row
Design: SamplerData
Issue(s): #13906
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.15.12
The system shall be able to ouptut samples from a sampler with
1. one column;
2. multiple columns;
3. large number of columns;
Specification(s): cols/one, cols/multi, cols/lots
Design: SamplerData
Issue(s): #20576
Collection(s): FUNCTIONAL
Type(s): CSVDiff

stochastic_tools: SobolStatistics
10.15.13The system shall support the ability to compute first, second, and total-effect Sobol sensitivity indices.
Specification(s): sobol
Design: SobolStatistics
Issue(s): #14784
Collection(s): FUNCTIONAL
Type(s): CSVDiff
10.15.14The system shall support the ability to compute confidence intervals on Sobol sensitivity indices.
Specification(s): sobol_bootstrap
Design: SobolStatistics
Issue(s): #14784
Collection(s): FUNCTIONAL
Type(s): CSVDiff

References

ISO/IEC/IEEE 24765:2010(E). Systems and software engineering—Vocabulary. first edition, December 15 2010.

@manual{ISO-systems-software,
    author = "24765:2010(E), ISO/IEC/IEEE",
    title = "Systems and software engineering---Vocabulary",
    edition = "First",
    year = "2010",
    month = "December 15"
}

ASME NQA-1. ASME NQA-1-2008 with the NQA-1a-2009 addenda: Quality Assurance Requirements for Nuclear Facility Applications. first edition, August 31 2009.

@manual{ASME-NQA-1-2008,
    author = "NQA-1, ASME",
    title = "ASME NQA-1-2008 with the NQA-1a-2009 addenda: Quality Assurance Requirements for Nuclear Facility Applications",
    orginization = "American Socieity of Mechanical Engineers",
    year = "2009",
    month = "August 31",
    edition = "First"
}

[csv_indices]
  type = CSVDiff
  input = csv.i
  cli_args = 'ParameterStudy/csv_column_indices=\'0 1\' Outputs/file_base=monte_carlo_csv'
  csvdiff = 'monte_carlo_csv_study_results_0001.csv'
  detail = 'CSV when specifiying column indices;'
[]

[csv_names]
  type = CSVDiff
  input = csv.i
  cli_args = 'ParameterStudy/csv_column_names=\'study_sampler_0 study_sampler_1\' Outputs/file_base=monte_carlo_csv'
  csvdiff = 'monte_carlo_csv_study_results_0001.csv'
  detail = 'CSV when specifiying column names;'
[]

[monte_carlo_num_samples]
  type = RunException
  input = sampling_error.i
  cli_args = 'ParameterStudy/sampling_type=monte-carlo ParameterStudy/distributions=uniform'
  expect_err = 'The num_samples parameter is required to build the requested sampling type\.'
  detail = '\'num_samples\' parameter for Monte Carlo sampling;'
[]

[monte_carlo_distributions]
  type = RunException
  input = sampling_error.i
  cli_args = 'ParameterStudy/sampling_type=monte-carlo ParameterStudy/num_samples=1'
  expect_err = 'The distributions parameter is required to build the requested sampling type\.'
  detail = '\'distributions\' parameter for Monte Carlo sampling;'
[]

[monte_carlo_unused]
  type = RunException
  input = sampling_error.i
  cli_args = 'ParameterStudy/sampling_type=monte-carlo
                  ParameterStudy/num_samples=1
                  ParameterStudy/distributions=uniform
                  ParameterStudy/linear_space_items=\'0 1 1\''
  expect_err = 'The following parameters are unused for the selected sampling type:'
  detail = '\'linear_space_items\' parameter with Monte Carlo sampling;'
[]

[lhs_num_samples]
  type = RunException
  input = sampling_error.i
  cli_args = 'ParameterStudy/sampling_type=lhs ParameterStudy/distributions=uniform'
  expect_err = 'The num_samples parameter is required to build the requested sampling type\.'
  detail = '\'num_samples\' parameter for latin hypercube sampling;'
[]

[lhs_distributions]
  type = RunException
  input = sampling_error.i
  cli_args = 'ParameterStudy/sampling_type=lhs ParameterStudy/num_samples=1'
  expect_err = 'The distributions parameter is required to build the requested sampling type\.'
  detail = '\'distributions\' parameter for latin hypercube sampling;'
[]

[cartesian_product]
  type = RunException
  input = sampling_error.i
  cli_args = 'ParameterStudy/sampling_type=cartesian-product'
  expect_err = 'The linear_space_items parameter is required to build the requested sampling type\.'
  detail = '\'linear_space_items\' parameter for cartesian product sampling;'
[]

[csv]
  type = RunException
  input = sampling_error.i
  cli_args = 'ParameterStudy/sampling_type=csv'
  expect_err = 'The csv_samples_file parameter is required to build the requested sampling type\.'
  detail = '\'samples_file\' parameter for CSV sampling;'
[]

[csv_both]
  type = RunException
  input = sampling_error.i
  cli_args = 'ParameterStudy/sampling_type=csv
                  ParameterStudy/csv_samples_file=gold/monte_carlo_csv_study_samples_0001.csv
                  ParameterStudy/csv_column_indices=1
                  ParameterStudy/csv_column_names=foo'
  expect_err = '\'csv_column_indices\' and \'csv_column_names\' cannot both be set\.'
  detail = 'both \'csv_column_indices\' and \'csv_column_names\' parameters are specified for CSV sampling;'
[]

[input_matrix]
  type = RunException
  input = sampling_error.i
  cli_args = 'ParameterStudy/sampling_type=input-matrix'
  expect_err = 'The input_matrix parameter is required to build the requested sampling type\.'
  detail = '\'matrix\' parameter for input matrix sampling;'
[]

[distributions]
  type = CSVDiff
  input = distributions.i
  csvdiff = distributions_csv_study_results_0001.csv
  requirement = 'The system shall be able to setup a Monte Carlo parameter study with uniform, normal, Weibull, lognormal, and truncated normal distributions.'
[]

[missing]
  type = RunException
  input = distributions_error.i
  cli_args = 'ParameterStudy/distributions=\'uniform uniform\'
                  ParameterStudy/uniform_upper_bound=\'1 1\''
  expect_err = 'The uniform_lower_bound parameter is required to build the listed distributions\.'
  detail = 'missing;'
[]

[inconsistent]
  type = RunException
  input = distributions_error.i
  cli_args = 'ParameterStudy/distributions=\'uniform uniform\'
                  ParameterStudy/uniform_lower_bound=\'0\'
                  ParameterStudy/uniform_upper_bound=\'1 1\''
  expect_err = 'The number of entries in uniform_lower_bound does not match the number of required entries \(\d+\) to build the listed distributions\.'
  detail = 'inconsitent size;'
[]

[unused]
  type = RunException
  input = distributions_error.i
  cli_args = 'ParameterStudy/distributions=\'uniform uniform\'
                  ParameterStudy/uniform_lower_bound=\'0 0\'
                  ParameterStudy/uniform_upper_bound=\'1 1\'
                  ParameterStudy/normal_mean=1'
  expect_err = 'The following parameters are unused for the listed distributions:'
  detail = 'unused;'
[]

[true]
  type = JSONDiff
  input = monte_carlo.i
  cli_args = 'ParameterStudy/output_type=json
                  ParameterStudy/show_study_objects=true
                  --color off'
  jsondiff = monte_carlo_json.json
  rel_err = 1e-4
  expect_out = 'Type:\s+StatisticsReporter'
  detail = 'compute statistics;'
[]

[false]
  type = RunApp
  input = monte_carlo.i
  cli_args = 'ParameterStudy/compute_statistics=false
                  ParameterStudy/show_study_objects=true
                  --color off'
  absent_out = 'Type:\s+StatisticsReporter'
  detail = 'not compute statistics;'
[]

[unused]
  type = RunException
  input = monte_carlo.i
  cli_args = 'ParameterStudy/compute_statistics=false
                  ParameterStudy/statistics=mean
                  ParameterStudy/ci_levels=0.1
                  ParameterStudy/ci_replicates=10'
  expect_err = 'The following parameters are unused since statistics are not being computed: ci_levels, ci_replicates, statistics'
  detail = 'error if there are unused statistics parameters;'
[]

[none]
  type = RunApp
  input = monte_carlo.i
  cli_args = 'ParameterStudy/output_type=none
                  ParameterStudy/compute_statistics=false
                  ParameterStudy/show_study_objects=true
                  --color off'
  expect_out = 'outputs : none'
  absent_out = 'Base Type:\s+Output'
  detail = 'no output;'
[]

[csv]
  type = CSVDiff
  input = monte_carlo.i
  cli_args = 'ParameterStudy/output_type=csv
                  ParameterStudy/compute_statistics=false
                  ParameterStudy/show_study_objects=true
                  --color off'
  csvdiff = 'monte_carlo_csv_study_results_0001.csv'
  expect_out = 'Type:\s+CSV'
  absent_out = 'Type:\s+JSON'
  detail = 'CSV output;'
[]

[json]
  type = JSONDiff
  input = monte_carlo.i
  cli_args = 'ParameterStudy/output_type=json
                  ParameterStudy/show_study_objects=true
                  --color off'
  jsondiff = monte_carlo_json.json
  rel_err = 1e-4
  expect_out = 'Type:\s+JSON'
  absent_out = 'Type:\s+CSV'
  detail = 'JSON output;'
[]

[normal]
  type = CSVDiff
  input = monte_carlo.i
  cli_args = 'ParameterStudy/show_study_objects=true ParameterStudy/multiapp_mode=normal --color off'
  csvdiff = 'monte_carlo_csv_study_results_0001.csv'
  expect_out = 'mode : normal'
  detail = 'one sub-app per sample;'
[]

[batch_reset]
  type = CSVDiff
  input = monte_carlo.i
  cli_args = 'ParameterStudy/show_study_objects=true ParameterStudy/multiapp_mode=batch-reset --color off'
  csvdiff = 'monte_carlo_csv_study_results_0001.csv'
  expect_out = 'mode : batch-reset'
  detail = 'batches with one sub-app per processor while re-initializing;'
[]

[batch_restore]
  type = CSVDiff
  input = monte_carlo.i
  cli_args = 'ParameterStudy/show_study_objects=true ParameterStudy/multiapp_mode=batch-restore --color off'
  csvdiff = 'monte_carlo_csv_study_results_0001.csv'
  expect_out = 'mode : batch-restore'
  detail = 'batches one sub-app per processor while restoring;'
[]

[batch_keep_solution]
  type = CSVDiff
  input = monte_carlo.i
  cli_args = 'ParameterStudy/show_study_objects=true ParameterStudy/multiapp_mode=batch-keep-solution --color off'
  csvdiff = 'monte_carlo_csv_study_results_0001.csv'
  expect_out = 'keep_solution_during_restore : 1\n.*\n\s*mode : batch-restore'
  detail = 'batches one sub-app per processor while restoring and keeping solution;'
[]

[batch_no_restore]
  type = CSVDiff
  input = monte_carlo.i
  cli_args = 'ParameterStudy/show_study_objects=true ParameterStudy/multiapp_mode=batch-no-restore --color off'
  csvdiff = 'monte_carlo_csv_study_results_0001.csv'
  expect_out = 'mode : batch-restore\n\s*no_restore : 1'
  detail = 'batches one sub-app per processor without restoring;'
[]

[parsed]
  type = CSVDiff
  input = distributions.i
  cli_args = 'ParameterStudy/show_study_objects=true --color off'
  csvdiff = distributions_csv_study_results_0001.csv
  expect_out = 'mode : batch-reset'
  detail = 'parameters are parsed;'
[]

[not_controllable]
  type = CSVDiff
  input = monte_carlo_not_controllable.i
  cli_args = 'Outputs/file_base=monte_carlo_csv ParameterStudy/sampler_column_names=\'BCs_left_value BCs_right_value\' ParameterStudy/show_study_objects=true --color off'
  csvdiff = 'monte_carlo_csv_study_results_0001.csv'
  expect_out = 'mode : batch-reset'
  detail = 'parameters are not controllable;'
[]

[transient]
  type = CSVDiff
  input = monte_carlo_transient.i
  cli_args = 'ParameterStudy/show_study_objects=true --color off'
  csvdiff = 'monte_carlo_transient_csv_study_results_0001.csv'
  expect_out = 'mode : batch-restore'
  detail = 'parameters are controllable in a transient problem;'
[]

[pseudo_transient]
  type = CSVDiff
  input = monte_carlo_pseudo_transient.i
  cli_args = 'ParameterStudy/show_study_objects=true --color off'
  csvdiff = 'monte_carlo_pseudo_transient_csv_study_results_0001.csv'
  expect_out = 'keep_solution_during_restore : 1\n.*\n\s*mode : batch-restore'
  detail = 'parameters are controllable in a pseudo-transient problem;'
[]

[steady]
  type = CSVDiff
  input = monte_carlo.i
  cli_args = 'ParameterStudy/show_study_objects=true --color off'
  csvdiff = 'monte_carlo_csv_study_results_0001.csv'
  expect_out = 'mode : batch-restore\n\s*no_restore : 1'
  detail = 'parameters are controllable in a steady-state problem;'
[]

[eigen]
  type = CSVDiff
  input = monte_carlo_eigen.i
  cli_args = 'ParameterStudy/show_study_objects=true --color off'
  csvdiff = 'monte_carlo_eigen_csv_study_results_0001.csv'
  expect_out = 'mode : batch-restore\n\s*no_restore : 1'
  detail = 'parameters are controllable in a eigenvalue problem;'
[]

[create]
  type = CheckFiles
  input = surrogate.i
  check_files = 'surrogate_trainer_poly_regression.rd/data'
  requirement = 'The system shall be able to create a surrogate that can be read in by file and use in aux kernels.'
[]

[parameter_unspecified]
  type = RunException
  input = surrogate_aux.i
  cli_args = 'AuxKernels/u_aux/parameters=\'funx 3.14 var pp foo\''
  prereq = create
  expect_err = '\'foo\' is not listed in \'scalar_parameters\'\, \'coupled_variables\'\, or \'coupled_array_variables\'.'
  detail = 'a parameter is undefined,'
[]

[scalar_missing]
  type = RunException
  input = surrogate_aux.i
  cli_args = 'AuxKernels/u_aux/parameters=\'funx 3.14 var pp foo\' AuxKernels/u_aux/scalar_parameters=\'funx funy pp foo\''
  prereq = create
  expect_err = '\'foo\' is not a postprocessor or a function.'
  detail = 'a scalar parameter is not found, or'
[]

[wrong_array_size]
  type = RunException
  input = surrogate_array_aux.i
  cli_args = 'AuxVariables/u/components=3'
  prereq = create
  expect_err = 'The number of components in \'\w+\' \(\d+\) does not match the number of components in \'\w+\' \(\d+\).'
  detail = 'a coupled array variable does not match the number of components.'
[]

[scalar_surrogate_aux]
  type = 'CSVDiff'
  input = 'surrogate_scalar_aux.i'
  csvdiff = 'surrogate_scalar_aux_out.csv'
  abs_zero = 1e-06
  group = 'simple'
  recover = false
  requirement = 'The system shall be able to evaluate a surrogate at given scalar variable values and insert the result into a scalar variable'
[]

[scalar_surrogate_aux_pp_arg]
  type = 'CSVDiff'
  input = 'surrogate_scalar_aux.i'
  csvdiff = 'scalar_surrogate_aux_pp_arg.csv'
  abs_zero = 1e-06
  group = 'simple'
  recover = false
  cli_args = 'Outputs/file_base=scalar_surrogate_aux_pp_arg AuxScalarKernels/scalar_eval/parameters="Tpp 5"'
  requirement = 'The system shall be able to evaluate a surrogate at given postprocessor values and insert the result into a scalar variable'
[]

[read-parameters]
  type = CSVDiff
  input = libtorch_drl_control.i
  csvdiff = parameter_read.csv
  cli_args = "Outputs/file_base='parameter_read'"
  requirement = "The system shall be able to read a neural network from a parameter file and use it to control a transient process."
  libtorch = true
[]

[without-nn]
  type = CSVDiff
  input = libtorch_drl_control.i
  csvdiff = run_wo_nn.csv
  cli_args = "Outputs/file_base='run_wo_nn' Controls/inactive='src_control' Postprocessors/left_flux/control_name='src_control_empty' Postprocessors/log_prob_left_flux/control_name='src_control_empty'"
  requirement = "The system shall be able to run with a DRL controller without having a neural net initialized in it."
  libtorch = true
[]

[kernel1d_gaussian]
  type = CSVDiff
  input = kernel_density_1d_gaussian.i
  csvdiff = 'kernel_density_1d_gaussian_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data file as input,'
[]

[kernel1d_uniform]
  type = CSVDiff
  input = kernel_density_1d_uniform.i
  csvdiff = 'kernel_density_1d_uniform_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Uniform kernel and data file as input,'
[]

[kernel1d_datvec]
  type = CSVDiff
  input = kernel_density_1d_datvec.i
  csvdiff = 'kernel_density_1d_datvec_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data vector as input,'
[]

[kernel1d_userbw]
  type = CSVDiff
  input = kernel_density_1d_userbw.i
  csvdiff = 'kernel_density_1d_userbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and user defined bandwidth,'
[]

[kernel1d_sdbw]
  type = CSVDiff
  input = kernel_density_1d_sdbw.i
  csvdiff = 'kernel_density_1d_sdbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,'
[]

[kernel1d_gaussian]
  type = CSVDiff
  input = kernel_density_1d_gaussian.i
  csvdiff = 'kernel_density_1d_gaussian_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data file as input,'
[]

[kernel1d_uniform]
  type = CSVDiff
  input = kernel_density_1d_uniform.i
  csvdiff = 'kernel_density_1d_uniform_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Uniform kernel and data file as input,'
[]

[kernel1d_datvec]
  type = CSVDiff
  input = kernel_density_1d_datvec.i
  csvdiff = 'kernel_density_1d_datvec_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data vector as input,'
[]

[kernel1d_userbw]
  type = CSVDiff
  input = kernel_density_1d_userbw.i
  csvdiff = 'kernel_density_1d_userbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and user defined bandwidth,'
[]

[kernel1d_sdbw]
  type = CSVDiff
  input = kernel_density_1d_sdbw.i
  csvdiff = 'kernel_density_1d_sdbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,'
[]

[kernel1d_gaussian]
  type = CSVDiff
  input = kernel_density_1d_gaussian.i
  csvdiff = 'kernel_density_1d_gaussian_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data file as input,'
[]

[kernel1d_uniform]
  type = CSVDiff
  input = kernel_density_1d_uniform.i
  csvdiff = 'kernel_density_1d_uniform_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Uniform kernel and data file as input,'
[]

[kernel1d_datvec]
  type = CSVDiff
  input = kernel_density_1d_datvec.i
  csvdiff = 'kernel_density_1d_datvec_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data vector as input,'
[]

[kernel1d_userbw]
  type = CSVDiff
  input = kernel_density_1d_userbw.i
  csvdiff = 'kernel_density_1d_userbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and user defined bandwidth,'
[]

[kernel1d_sdbw]
  type = CSVDiff
  input = kernel_density_1d_sdbw.i
  csvdiff = 'kernel_density_1d_sdbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,'
[]

[normal_direct]
  type = CSVDiff
  input = normal_direct.i
  csvdiff = 'normal_direct_out.csv'
  allow_test_objects = true
  requirement = 'The system shall provide a normal distribution with the ability to directly call methods with distribution inputs.'
  design = 'Normal.md'
  issues = '#12720'
[]

[normal_direct_type_error]
  type = RunException
  input = normal_direct_type_error.i
  allow_test_objects = true
  expect_err = "The 'cdf' object failed to retrieve 'this_is_the_wrong_type' distribution with the desired type."
  requirement = 'The system shall produce an error if a distribution is retrieved with the differing type than supplied.'
  design = 'Normal.md'
  issues = '#12720'
[]

[kernel1d_gaussian]
  type = CSVDiff
  input = kernel_density_1d_gaussian.i
  csvdiff = 'kernel_density_1d_gaussian_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data file as input,'
[]

[kernel1d_uniform]
  type = CSVDiff
  input = kernel_density_1d_uniform.i
  csvdiff = 'kernel_density_1d_uniform_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Uniform kernel and data file as input,'
[]

[kernel1d_datvec]
  type = CSVDiff
  input = kernel_density_1d_datvec.i
  csvdiff = 'kernel_density_1d_datvec_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data vector as input,'
[]

[kernel1d_userbw]
  type = CSVDiff
  input = kernel_density_1d_userbw.i
  csvdiff = 'kernel_density_1d_userbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and user defined bandwidth,'
[]

[kernel1d_sdbw]
  type = CSVDiff
  input = kernel_density_1d_sdbw.i
  csvdiff = 'kernel_density_1d_sdbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,'
[]

[kernel1d_gaussian]
  type = CSVDiff
  input = kernel_density_1d_gaussian.i
  csvdiff = 'kernel_density_1d_gaussian_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data file as input,'
[]

[kernel1d_uniform]
  type = CSVDiff
  input = kernel_density_1d_uniform.i
  csvdiff = 'kernel_density_1d_uniform_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Uniform kernel and data file as input,'
[]

[kernel1d_datvec]
  type = CSVDiff
  input = kernel_density_1d_datvec.i
  csvdiff = 'kernel_density_1d_datvec_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data vector as input,'
[]

[kernel1d_userbw]
  type = CSVDiff
  input = kernel_density_1d_userbw.i
  csvdiff = 'kernel_density_1d_userbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and user defined bandwidth,'
[]

[kernel1d_sdbw]
  type = CSVDiff
  input = kernel_density_1d_sdbw.i
  csvdiff = 'kernel_density_1d_sdbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,'
[]

[kernel1d_gaussian]
  type = CSVDiff
  input = kernel_density_1d_gaussian.i
  csvdiff = 'kernel_density_1d_gaussian_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data file as input,'
[]

[kernel1d_uniform]
  type = CSVDiff
  input = kernel_density_1d_uniform.i
  csvdiff = 'kernel_density_1d_uniform_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Uniform kernel and data file as input,'
[]

[kernel1d_datvec]
  type = CSVDiff
  input = kernel_density_1d_datvec.i
  csvdiff = 'kernel_density_1d_datvec_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data vector as input,'
[]

[kernel1d_userbw]
  type = CSVDiff
  input = kernel_density_1d_userbw.i
  csvdiff = 'kernel_density_1d_userbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and user defined bandwidth,'
[]

[kernel1d_sdbw]
  type = CSVDiff
  input = kernel_density_1d_sdbw.i
  csvdiff = 'kernel_density_1d_sdbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,'
[]

[kernel1d_gaussian]
  type = CSVDiff
  input = kernel_density_1d_gaussian.i
  csvdiff = 'kernel_density_1d_gaussian_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data file as input,'
[]

[kernel1d_uniform]
  type = CSVDiff
  input = kernel_density_1d_uniform.i
  csvdiff = 'kernel_density_1d_uniform_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Uniform kernel and data file as input,'
[]

[kernel1d_datvec]
  type = CSVDiff
  input = kernel_density_1d_datvec.i
  csvdiff = 'kernel_density_1d_datvec_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data vector as input,'
[]

[kernel1d_userbw]
  type = CSVDiff
  input = kernel_density_1d_userbw.i
  csvdiff = 'kernel_density_1d_userbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and user defined bandwidth,'
[]

[kernel1d_sdbw]
  type = CSVDiff
  input = kernel_density_1d_sdbw.i
  csvdiff = 'kernel_density_1d_sdbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,'
[]

[kernel1d_gaussian]
  type = CSVDiff
  input = kernel_density_1d_gaussian.i
  csvdiff = 'kernel_density_1d_gaussian_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data file as input,'
[]

[kernel1d_uniform]
  type = CSVDiff
  input = kernel_density_1d_uniform.i
  csvdiff = 'kernel_density_1d_uniform_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Uniform kernel and data file as input,'
[]

[kernel1d_datvec]
  type = CSVDiff
  input = kernel_density_1d_datvec.i
  csvdiff = 'kernel_density_1d_datvec_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data vector as input,'
[]

[kernel1d_userbw]
  type = CSVDiff
  input = kernel_density_1d_userbw.i
  csvdiff = 'kernel_density_1d_userbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and user defined bandwidth,'
[]

[kernel1d_sdbw]
  type = CSVDiff
  input = kernel_density_1d_sdbw.i
  csvdiff = 'kernel_density_1d_sdbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,'
[]

[kernel1d_gaussian]
  type = CSVDiff
  input = kernel_density_1d_gaussian.i
  csvdiff = 'kernel_density_1d_gaussian_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data file as input,'
[]

[kernel1d_uniform]
  type = CSVDiff
  input = kernel_density_1d_uniform.i
  csvdiff = 'kernel_density_1d_uniform_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Uniform kernel and data file as input,'
[]

[kernel1d_datvec]
  type = CSVDiff
  input = kernel_density_1d_datvec.i
  csvdiff = 'kernel_density_1d_datvec_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data vector as input,'
[]

[kernel1d_userbw]
  type = CSVDiff
  input = kernel_density_1d_userbw.i
  csvdiff = 'kernel_density_1d_userbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and user defined bandwidth,'
[]

[kernel1d_sdbw]
  type = CSVDiff
  input = kernel_density_1d_sdbw.i
  csvdiff = 'kernel_density_1d_sdbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,'
[]

[kernel1d_gaussian]
  type = CSVDiff
  input = kernel_density_1d_gaussian.i
  csvdiff = 'kernel_density_1d_gaussian_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data file as input,'
[]

[kernel1d_uniform]
  type = CSVDiff
  input = kernel_density_1d_uniform.i
  csvdiff = 'kernel_density_1d_uniform_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Uniform kernel and data file as input,'
[]

[kernel1d_datvec]
  type = CSVDiff
  input = kernel_density_1d_datvec.i
  csvdiff = 'kernel_density_1d_datvec_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and data vector as input,'
[]

[kernel1d_userbw]
  type = CSVDiff
  input = kernel_density_1d_userbw.i
  csvdiff = 'kernel_density_1d_userbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and user defined bandwidth,'
[]

[kernel1d_sdbw]
  type = CSVDiff
  input = kernel_density_1d_sdbw.i
  csvdiff = 'kernel_density_1d_sdbw_out.csv'
  allow_test_objects = true
  abs_zero = 5e-7
  rel_err = 1e-3

  detail = 'Kernel Density 1D with a Gaussian kernel and standard deviation as bandwidth,'
[]

[libtorch_drl_reward_linear]
  type = CSVDiff
  input = drl_reward.i
  csvdiff = drl_reward_linear.csv
  requirement = "The system shall be able to evaluate a linear DRL reward function with a given, time-dependent target."
  issues = '#19571'
  design = 'ScaledAbsDifferenceDRLRewardFunction.md'
  cli_args = "Outputs/file_base='drl_reward_linear'"
  libtorch = true
[]

[generate]
  type = 'CSVDiff'
  input = 'random_ic_distribution_test.i'
  csvdiff = 'random_ic_distribution_test_initial_histo_0000.csv'
  mesh_mode = 'replicated' # sensitive to difference in partitioning

  requirement = 'The system shall generate parallel agnostic random initial conditions using a distribution function.'
[]

[test_err_distribution_and_min_max]
  type = 'RunException'
  input = 'random_ic_distribution_test.i'
  cli_args = 'ICs/u_aux/min=0 ICs/u_aux/max=1'
  prereq = 'generate'
  expect_err = "Cannot use together with 'min' or 'max' parameter"

  requirement = 'The system shall generate an error the random initial condition is used with both a distribution and min or max value defined.'
[]

[loglikelihood_scalar]
  type = 'JSONDiff'
  input = main.i
  allow_test_objects = true
  jsondiff = 'loglikelihood_scalar.json'
  min_parallel = 2
  max_parallel = 2
  detail = 'using a Gaussian type with log-likelihoods requested and model/experiment values are scalars; '
[]

[likelihood_scalar]
  type = 'JSONDiff'
  input = main.i
  allow_test_objects = true
  jsondiff = 'likelihood_scalar.json'
  cli_args = "Likelihood/gaussian/log_likelihood='false' Outputs/file_base='likelihood_scalar'"
  min_parallel = 2
  max_parallel = 2
  detail = 'using a Gaussian type with likelihoods requested and model/experiment values are scalars; '
[]

[truncatedgaussian]
  type = 'JSONDiff'
  input = main.i
  allow_test_objects = true
  jsondiff = 'truncatedgaussian.json'
  cli_args = "Likelihood/gaussian/type='TruncatedGaussian' Likelihood/gaussian/lower_bound='-1.0' Likelihood/gaussian/upper_bound='1.0' Outputs/file_base='truncatedgaussian'"
  min_parallel = 2
  max_parallel = 2
  detail = 'using a truncated Gaussian type with likelihoods requested and model/experiment values are scalars. '
[]

[extremevalue]
  type = 'JSONDiff'
  input = main.i
  allow_test_objects = true
  jsondiff = 'extremevalue.json'
  cli_args = "Likelihood/gaussian/type='ExtremeValue' Outputs/file_base='extremevalue'"
  min_parallel = 2
  max_parallel = 2
  detail = 'using a Extreme Value type with likelihoods requested and model/experiment values are scalars. '
[]

[truncatedgaussian_bounds]
  type = RunException
  input = main.i
  allow_test_objects = True
  cli_args = "Likelihood/gaussian/type=TruncatedGaussian Likelihood/gaussian/lower_bound=1.0 Likelihood/gaussian/upper_bound=-1.0"
  detail = 'the specified lower bound is greater than the upper bound; '
  expect_err = "The specified lower bound should be less than the upper bound."
[]

[loglikelihood_scalar]
  type = 'JSONDiff'
  input = main.i
  allow_test_objects = true
  jsondiff = 'loglikelihood_scalar.json'
  min_parallel = 2
  max_parallel = 2
  detail = 'using a Gaussian type with log-likelihoods requested and model/experiment values are scalars; '
[]

[likelihood_scalar]
  type = 'JSONDiff'
  input = main.i
  allow_test_objects = true
  jsondiff = 'likelihood_scalar.json'
  cli_args = "Likelihood/gaussian/log_likelihood='false' Outputs/file_base='likelihood_scalar'"
  min_parallel = 2
  max_parallel = 2
  detail = 'using a Gaussian type with likelihoods requested and model/experiment values are scalars; '
[]

[truncatedgaussian]
  type = 'JSONDiff'
  input = main.i
  allow_test_objects = true
  jsondiff = 'truncatedgaussian.json'
  cli_args = "Likelihood/gaussian/type='TruncatedGaussian' Likelihood/gaussian/lower_bound='-1.0' Likelihood/gaussian/upper_bound='1.0' Outputs/file_base='truncatedgaussian'"
  min_parallel = 2
  max_parallel = 2
  detail = 'using a truncated Gaussian type with likelihoods requested and model/experiment values are scalars. '
[]

[extremevalue]
  type = 'JSONDiff'
  input = main.i
  allow_test_objects = true
  jsondiff = 'extremevalue.json'
  cli_args = "Likelihood/gaussian/type='ExtremeValue' Outputs/file_base='extremevalue'"
  min_parallel = 2
  max_parallel = 2
  detail = 'using a Extreme Value type with likelihoods requested and model/experiment values are scalars. '
[]

[truncatedgaussian_bounds]
  type = RunException
  input = main.i
  allow_test_objects = True
  cli_args = "Likelihood/gaussian/type=TruncatedGaussian Likelihood/gaussian/lower_bound=1.0 Likelihood/gaussian/upper_bound=-1.0"
  detail = 'the specified lower bound is greater than the upper bound; '
  expect_err = "The specified lower bound should be less than the upper bound."
[]

[loglikelihood_scalar]
  type = 'JSONDiff'
  input = main.i
  allow_test_objects = true
  jsondiff = 'loglikelihood_scalar.json'
  min_parallel = 2
  max_parallel = 2
  detail = 'using a Gaussian type with log-likelihoods requested and model/experiment values are scalars; '
[]

[likelihood_scalar]
  type = 'JSONDiff'
  input = main.i
  allow_test_objects = true
  jsondiff = 'likelihood_scalar.json'
  cli_args = "Likelihood/gaussian/log_likelihood='false' Outputs/file_base='likelihood_scalar'"
  min_parallel = 2
  max_parallel = 2
  detail = 'using a Gaussian type with likelihoods requested and model/experiment values are scalars; '
[]

[truncatedgaussian]
  type = 'JSONDiff'
  input = main.i
  allow_test_objects = true
  jsondiff = 'truncatedgaussian.json'
  cli_args = "Likelihood/gaussian/type='TruncatedGaussian' Likelihood/gaussian/lower_bound='-1.0' Likelihood/gaussian/upper_bound='1.0' Outputs/file_base='truncatedgaussian'"
  min_parallel = 2
  max_parallel = 2
  detail = 'using a truncated Gaussian type with likelihoods requested and model/experiment values are scalars. '
[]

[extremevalue]
  type = 'JSONDiff'
  input = main.i
  allow_test_objects = true
  jsondiff = 'extremevalue.json'
  cli_args = "Likelihood/gaussian/type='ExtremeValue' Outputs/file_base='extremevalue'"
  min_parallel = 2
  max_parallel = 2
  detail = 'using a Extreme Value type with likelihoods requested and model/experiment values are scalars. '
[]

[truncatedgaussian_bounds]
  type = RunException
  input = main.i
  allow_test_objects = True
  cli_args = "Likelihood/gaussian/type=TruncatedGaussian Likelihood/gaussian/lower_bound=1.0 Likelihood/gaussian/upper_bound=-1.0"
  detail = 'the specified lower bound is greater than the upper bound; '
  expect_err = "The specified lower bound should be less than the upper bound."
[]

[single_batch]
  type = CSVDiff
  input = parent_single.i
  csvdiff = 'parent_single_out_storage_0002.csv'
  prereq = parent/single_normal
  max_parallel = 3

  detail = 'for a single parameter for a batch of sub-applications,'
[]

[multiple_normal]
  type = CSVDiff
  input = parent_multiple.i
  cli_args = MultiApps/sub/mode=normal
  csvdiff = 'parent_multiple_out_storage_0002.csv'
  prereq = parent/single_batch

  detail = 'for multiple parameters, and'
[]

[vector]
  type = CSVDiff
  input = parent_vector.i
  csvdiff = 'parent_vector_out_storage_0002.csv parent_vector_out_prop_A_0002.csv parent_vector_out_prop_B_0002.csv parent_vector_out_prop_C_0002.csv parent_vector_out_prop_D_0002.csv'
  max_parallel = 3

  detail = 'for vector parameters for a batch of sub-applications,'
[]

[multiple_batch]
  type = CSVDiff
  input = parent_multiple.i
  csvdiff = 'parent_multiple_out_storage_0002.csv'
  prereq = parent/multiple_normal
  max_parallel = 3

  detail = 'for multiple parameters for a batch of sub-applications.'
[]

[multiple_batch_with_index]
  type = CSVDiff
  input = parent_multiple.i
  csvdiff = 'parent_multiple_out_storage_0002.csv'
  cli_args = "Controls/cmdline/param_names='Mesh/ymax[1] Mesh/xmax[0]'"
  prereq = parent/multiple_normal
  max_parallel = 3

  detail = 'for multiple parameters using their global column indexes for a batch of sub-applications.'
[]

[wrong_batch_mode]
  type = RunException
  input = parent_single.i
  cli_args = MultiApps/sub/mode=batch-restore
  expect_err = "The MultiApp object, 'sub', supplied to the 'cmdline' object is setup to run in 'batch-restore' mode, when using this mode command line arguments cannot be modified; batch-reset mode should be used instead."
  requirement = "The system shall report an error when the supplied sampler object operates in a mode that does not allow for command line arguments to be modified."
  prereq = parent/multiple_batch
  max_parallel = 3
[]

[wrong_execute_on]
  type = RunException
  input = parent_single.i
  cli_args = Samplers/sample/execute_on=INITIAL
  expect_err = "The sampler object, 'sample', is being used by the 'cmdline' object, thus the 'execute_on' of the sampler must include 'PRE_MULTIAPP_SETUP'."
  requirement = "The system shall report an error when the supplied sampler does not use the correct execution flags."
  prereq = parent/multiple_batch
  max_parallel = 3
[]

[vector_no_bracket]
  type = RunException
  input = parent_vector.i
  cli_args = "Controls/cmdline/param_names='Mesh/xmax[0] Materials/const/prop_values[1,(1.5),2,2] Mesh/ymax'"
  expect_err = "If the bracket is used, it must be provided to every parameter."
  requirement = "The system shall report an error when '[]' syntax is not used for all parameters."
  max_parallel = 3
[]

[vector_out_of_bound]
  type = RunException
  input = parent_vector.i
  cli_args = "Controls/cmdline/param_names='Mesh/xmax[0] Materials/const/prop_values[1,(1.5),2,2] Mesh/ymax[4]'"
  expect_err = "The provided global column index \(4\) for Mesh/ymax is out of bound."
  requirement = "The system shall report an error when provided global column index is out of bound."
  max_parallel = 3
[]

[normal]
  type = CSVDiff
  input = parent_full_solve.i
  csvdiff = parent_full_solve_out_storage_0002.csv
  cli_args = MultiApps/runner/mode=normal
  requirement = "The system shall support pulling postprocessor data from a sub-application for each row of sampled data."
[]

[batch]
  type = CheckFiles
  input = parent_full_solve.i
  check_files = 'parent_full_solve_out_runner0.e parent_full_solve_out_runner1.e parent_full_solve_out_runner2.e parent_full_solve_out_runner3.e parent_full_solve_out_runner4.e'
  requirement = "The system shall support running sub-applications in batches."
[]

[n2_restore]
  type = CSVDiff
  input = parent_full_solve.i
  csvdiff = parent_full_solve_out_storage_0002.csv
  max_parallel = 2
  min_parallel = 2
  cli_args = "MultiApps/runner/mode=batch-restore"

  detail = 'on multiple processors using in-memory backup.'
[]

[error]
  type = RunException
  input = conditional_main.i
  cli_args = MultiApps/runner/mode=normal
  expect_err = "Conditionally run sampler multiapp only works in batch modes."
  detail = 'error if running in normal mode.'
[]

[batch-restore]
  type = JSONDiff
  input = main.i
  allow_test_objects = true
  cli_args = 'MultiApps/runner/mode=batch-restore'
  jsondiff = main_out.json

  detail = "batch execution with memory-based restoring, and"
[]

[batch-reset]
  type = JSONDiff
  input = main.i
  allow_test_objects = true
  cli_args = 'MultiApps/runner/mode=batch-reset'
  jsondiff = main_out.json

  detail = "batch execution with reset-based restoring."
[]

[error]
  type = RunException
  input = main.i
  allow_test_objects = true
  expect_err = "The size of the sampler has changed; SamplerTransientMultiApp object do not support dynamic Sampler output."

  requirement = "The system shall report an error when the size of a sampler is altered an sub-applications are progressing with time with the main application."
[]

[normal]
  type = CSVDiff
  input = 'stm_small_batch.i'
  cli_args = 'MultiApps/main/mode=normal main:Outputs/csv=true main:sub:Outputs/csv=true main:sub:subsub:Outputs/csv=true'
  csvdiff = 'stm_small_batch_out_main0.csv stm_small_batch_out_main0_sub0.csv stm_small_batch_out_main0_sub0_subsub0.csv stm_small_batch_out_main0_sub0_subsub1.csv
                 stm_small_batch_out_main1.csv stm_small_batch_out_main1_sub0.csv stm_small_batch_out_main1_sub0_subsub0.csv stm_small_batch_out_main1_sub0_subsub1.csv
                 stm_small_batch_out_main2.csv stm_small_batch_out_main2_sub0.csv stm_small_batch_out_main2_sub0_subsub0.csv stm_small_batch_out_main2_sub0_subsub1.csv
                 stm_small_batch_out_main3.csv stm_small_batch_out_main3_sub0.csv stm_small_batch_out_main3_sub0_subsub0.csv stm_small_batch_out_main3_sub0_subsub1.csv'
  detail = 'normal multiapp mode;'
[]

[batch_reset]
  type = CSVDiff
  input = 'stm_small_batch.i'
  cli_args = 'MultiApps/main/mode=batch-reset main:Outputs/csv=true main:sub:Outputs/csv=true main:sub:subsub:Outputs/csv=true'
  csvdiff = 'stm_small_batch_out_main0.csv stm_small_batch_out_main0_sub0.csv stm_small_batch_out_main0_sub0_subsub0.csv stm_small_batch_out_main0_sub0_subsub1.csv
                 stm_small_batch_out_main1.csv stm_small_batch_out_main1_sub0.csv stm_small_batch_out_main1_sub0_subsub0.csv stm_small_batch_out_main1_sub0_subsub1.csv
                 stm_small_batch_out_main2.csv stm_small_batch_out_main2_sub0.csv stm_small_batch_out_main2_sub0_subsub0.csv stm_small_batch_out_main2_sub0_subsub1.csv
                 stm_small_batch_out_main3.csv stm_small_batch_out_main3_sub0.csv stm_small_batch_out_main3_sub0_subsub0.csv stm_small_batch_out_main3_sub0_subsub1.csv'
  detail = 'batch-reset multiapp mode;'
[]

[batch_restore]
  type = CSVDiff
  input = 'stm_small_batch.i'
  cli_args = 'MultiApps/main/mode=batch-restore main:Outputs/csv=true main:sub:Outputs/csv=true main:sub:subsub:Outputs/csv=true'
  csvdiff = 'stm_small_batch_out_main0.csv stm_small_batch_out_main0_sub0.csv stm_small_batch_out_main0_sub0_subsub0.csv stm_small_batch_out_main0_sub0_subsub1.csv
                 stm_small_batch_out_main1.csv stm_small_batch_out_main1_sub0.csv stm_small_batch_out_main1_sub0_subsub0.csv stm_small_batch_out_main1_sub0_subsub1.csv
                 stm_small_batch_out_main2.csv stm_small_batch_out_main2_sub0.csv stm_small_batch_out_main2_sub0_subsub0.csv stm_small_batch_out_main2_sub0_subsub1.csv
                 stm_small_batch_out_main3.csv stm_small_batch_out_main3_sub0.csv stm_small_batch_out_main3_sub0_subsub0.csv stm_small_batch_out_main3_sub0_subsub1.csv'
  detail = 'batch-restore multiapp mode;'
[]

[normal_less_procs]
  type = CSVDiff
  input = main.i
  allow_test_objects = true
  csvdiff = main_out_vpp_0001.csv
  max_parallel = 5
  detail = 'with less processors than rows in normal mode;'
[]

[normal_more_procs]
  type = CSVDiff
  input = main.i
  allow_test_objects = true
  csvdiff = main_out_vpp_0001.csv
  min_parallel = 6
  detail = 'with more processors than rows in normal mode;'
[]

[normal_min_procs]
  type = CSVDiff
  input = main.i
  cli_args = 'MultiApps/sub/min_procs_per_app=2 Samplers/sample/min_procs_per_row=2'
  allow_test_objects = true
  csvdiff = main_out_vpp_0001.csv
  min_parallel = 4
  detail = 'with specified minimum processors per app in normal mode;'
[]

[reset_less_procs]
  type = CSVDiff
  input = main.i
  cli_args = 'MultiApps/sub/mode=batch-reset'
  allow_test_objects = true
  csvdiff = main_out_vpp_0001.csv
  max_parallel = 5
  detail = 'with less processors than rows in batch-reset mode;'
[]

[reset_more_procs]
  type = CSVDiff
  input = main.i
  cli_args = 'MultiApps/sub/mode=batch-reset'
  allow_test_objects = true
  csvdiff = main_out_vpp_0001.csv
  min_parallel = 6
  detail = 'with more processors than rows in batch-reset mode;'
[]

[reset_min_procs]
  type = CSVDiff
  input = main.i
  cli_args = 'MultiApps/sub/mode=batch-reset MultiApps/sub/min_procs_per_app=2 Samplers/sample/min_procs_per_row=2'
  allow_test_objects = true
  csvdiff = main_out_vpp_0001.csv
  min_parallel = 4
  detail = 'with specified minimum processors per app in batch-reset mode;'
[]

[restore_less_procs]
  type = CSVDiff
  input = main.i
  cli_args = 'MultiApps/sub/mode=batch-restore Controls/active="" Transfers/inactive=rep Reporters/check/reporters=vpp/pp:pp2'
  allow_test_objects = true
  csvdiff = main_out_vpp_0001.csv
  max_parallel = 5
  detail = 'with less processors than rows in batch-restore mode;'
[]

[restore_more_procs]
  type = CSVDiff
  input = main.i
  cli_args = 'MultiApps/sub/mode=batch-restore Controls/active="" Transfers/inactive=rep Reporters/check/reporters=vpp/pp:pp2'
  allow_test_objects = true
  csvdiff = main_out_vpp_0001.csv
  min_parallel = 6
  detail = 'with more processors than rows in batch-restore mode;'
[]

[restore_min_procs]
  type = CSVDiff
  input = main.i
  cli_args = 'MultiApps/sub/mode=batch-restore MultiApps/sub/min_procs_per_app=2 Samplers/sample/min_procs_per_row=2 Controls/active="" Transfers/inactive=rep Reporters/check/reporters=vpp/pp:pp2'
  allow_test_objects = true
  csvdiff = main_out_vpp_0001.csv
  min_parallel = 4
  detail = 'with specified minimum processors per app in batch-restore mode;'
[]

[error_min_procs]
  type = RunException
  input = main.i
  cli_args = 'MultiApps/sub/min_procs_per_app=2 Samplers/sample/min_procs_per_row=1'
  allow_test_objects = true
  expect_err = "Sampler and multiapp communicator configuration inconsistent. Please ensure that 'MultiApps/sub/min\(max\)_procs_per_app' and 'Samplers/sample/min\(max\)_procs_per_row' are the same."
  min_parallel = 2
  detail = 'error when partitionings do not match;'
[]

[full_solve]
  type = CheckFiles
  input = parent_full_solve.i
  check_files = 'parent_full_solve_out_runner0.e parent_full_solve_out_runner1.e parent_full_solve_out_runner2.e parent_full_solve_out_runner3.e parent_full_solve_out_runner4.e'

  requirement = 'The system shall provide the ability to create a full-solve type sub-application from sampled data from distributions.'
[]

[normal]
  type = JSONDiff
  input = main.i
  jsondiff = main_new_out.json
  cli_args = "Outputs/file_base=main_new_out Samplers/dynamic/execute_on='INITIAL TIMESTEP_END'"

  detail = "using normal operation;"
[]

[batch-restore]
  type = JSONDiff
  input = main.i
  jsondiff = main_new_out.json
  cli_args = "MultiApps/runner/mode=batch-restore Outputs/file_base=main_new_out Samplers/dynamic/execute_on='INITIAL TIMESTEP_END'"

  detail = "using in memory backup operation;"
[]

[batch-reset]
  type = JSONDiff
  input = main.i
  jsondiff = main_new_out.json
  cli_args = "MultiApps/runner/mode=batch-reset Outputs/file_base=main_new_out Samplers/dynamic/execute_on='INITIAL TIMESTEP_END'"

  detail = "using reset operation."
[]

[cmdline_batch]
  type = CSVDiff
  input = main_full_solve.i
  cli_args = "MultiApps/sub/mode=batch-reset"
  csvdiff = main_full_solve_out_storage_0001.csv
  detail = 'command line control in batch mode;'
[]

[transfer_normal]
  type = CSVDiff
  input = main_full_solve.i
  cli_args = "Transfers/inactive='' Controls/active=''"
  csvdiff = main_full_solve_out_storage_0001.csv
  detail = 'parameter transfer in normal mode;'
[]

[transfer_batch_reset]
  type = CSVDiff
  input = main_full_solve.i
  cli_args = "MultiApps/sub/mode=batch-reset Transfers/inactive='' Controls/active=''"
  csvdiff = main_full_solve_out_storage_0001.csv
  detail = 'parameter transfer in batch-reset mode;'
[]

[transfer_batch_restore]
  type = CSVDiff
  input = main_full_solve.i
  cli_args = "MultiApps/sub/mode=batch-restore Transfers/inactive='' Controls/active=''"
  csvdiff = main_full_solve_out_storage_0001.csv
  detail = 'parameter transfer in batch-restore mode;'
[]

[constant]
  type = JSONDiff
  input = main.i
  jsondiff = main_constant_size_out.json
  allow_test_objects = true
  cli_args = "Samplers/dynamic/increment_rows=0 Outputs/file_base=main_constant_size_out"

  detail = "that remain constant in size and"
[]

[single_batch]
  type = CSVDiff
  input = parent_single.i
  csvdiff = 'parent_single_out_storage_0002.csv'
  prereq = parent/single_normal
  max_parallel = 3

  detail = 'for a single parameter for a batch of sub-applications,'
[]

[multiple_normal]
  type = CSVDiff
  input = parent_multiple.i
  cli_args = MultiApps/sub/mode=normal
  csvdiff = 'parent_multiple_out_storage_0002.csv'
  prereq = parent/single_batch

  detail = 'for multiple parameters, and'
[]

[vector]
  type = CSVDiff
  input = parent_vector.i
  csvdiff = 'parent_vector_out_storage_0002.csv parent_vector_out_prop_A_0002.csv parent_vector_out_prop_B_0002.csv parent_vector_out_prop_C_0002.csv parent_vector_out_prop_D_0002.csv'
  max_parallel = 3

  detail = 'for vector parameters for a batch of sub-applications,'
[]

[multiple_batch]
  type = CSVDiff
  input = parent_multiple.i
  csvdiff = 'parent_multiple_out_storage_0002.csv'
  prereq = parent/multiple_normal
  max_parallel = 3

  detail = 'for multiple parameters for a batch of sub-applications.'
[]

[multiple_batch_with_index]
  type = CSVDiff
  input = parent_multiple.i
  csvdiff = 'parent_multiple_out_storage_0002.csv'
  cli_args = "Controls/cmdline/param_names='Mesh/ymax[1] Mesh/xmax[0]'"
  prereq = parent/multiple_normal
  max_parallel = 3

  detail = 'for multiple parameters using their global column indexes for a batch of sub-applications.'
[]

[wrong_batch_mode]
  type = RunException
  input = parent_single.i
  cli_args = MultiApps/sub/mode=batch-restore
  expect_err = "The MultiApp object, 'sub', supplied to the 'cmdline' object is setup to run in 'batch-restore' mode, when using this mode command line arguments cannot be modified; batch-reset mode should be used instead."
  requirement = "The system shall report an error when the supplied sampler object operates in a mode that does not allow for command line arguments to be modified."
  prereq = parent/multiple_batch
  max_parallel = 3
[]

[wrong_execute_on]
  type = RunException
  input = parent_single.i
  cli_args = Samplers/sample/execute_on=INITIAL
  expect_err = "The sampler object, 'sample', is being used by the 'cmdline' object, thus the 'execute_on' of the sampler must include 'PRE_MULTIAPP_SETUP'."
  requirement = "The system shall report an error when the supplied sampler does not use the correct execution flags."
  prereq = parent/multiple_batch
  max_parallel = 3
[]

[vector_no_bracket]
  type = RunException
  input = parent_vector.i
  cli_args = "Controls/cmdline/param_names='Mesh/xmax[0] Materials/const/prop_values[1,(1.5),2,2] Mesh/ymax'"
  expect_err = "If the bracket is used, it must be provided to every parameter."
  requirement = "The system shall report an error when '[]' syntax is not used for all parameters."
  max_parallel = 3
[]

[vector_out_of_bound]
  type = RunException
  input = parent_vector.i
  cli_args = "Controls/cmdline/param_names='Mesh/xmax[0] Materials/const/prop_values[1,(1.5),2,2] Mesh/ymax[4]'"
  expect_err = "The provided global column index \(4\) for Mesh/ymax is out of bound."
  requirement = "The system shall report an error when provided global column index is out of bound."
  max_parallel = 3
[]

[multiple]
  type = CSVDiff
  input = parent_multiple.i
  csvdiff = 'parent_multiple_out_sub0.csv parent_multiple_out_sub1.csv parent_multiple_out_sub2.csv'

  detail = 'for a multiple parameters.'
[]

[wrong_multiapp_type]
  type = RunException
  input = parent_wrong_multiapp_type.i
  expect_err = "The MultiApp must construct its sub-apps in initial setup but not during its creation"

  requirement = "The system shall report an error when sub-apps are constructed too early thus unable to be changed by samplers."
[]

[wrong_num_params]
  type = RunException
  input = parent_wrong_num_params.i
  expect_err = "The number of columns \(2\) must match the number of parameters \(3\)"

  requirement = "The system shall report an error when the number of samples differs from the number of command line parameters."
[]

[normal]
  type = CSVDiff
  input = parent_full_solve.i
  csvdiff = parent_full_solve_out_storage_0002.csv
  cli_args = MultiApps/runner/mode=normal
  requirement = "The system shall support pulling postprocessor data from a sub-application for each row of sampled data."
[]

[batch]
  type = CheckFiles
  input = parent_full_solve.i
  check_files = 'parent_full_solve_out_runner0.e parent_full_solve_out_runner1.e parent_full_solve_out_runner2.e parent_full_solve_out_runner3.e parent_full_solve_out_runner4.e'
  requirement = "The system shall support running sub-applications in batches."
[]

[n2_restore]
  type = CSVDiff
  input = parent_full_solve.i
  csvdiff = parent_full_solve_out_storage_0002.csv
  max_parallel = 2
  min_parallel = 2
  cli_args = "MultiApps/runner/mode=batch-restore"

  detail = 'on multiple processors using in-memory backup.'
[]

[sobol_from_multiapp]
  type = CSVDiff
  input = parent.i
  csvdiff = 'parent_out_storage_0001.csv parent_out_storage_0002.csv parent_out_storage_0003.csv parent_out_storage_0004.csv parent_out_storage_0005.csv'

  requirement = "The system shall support the ability to transfer a single value from each sub-application for a set of stochastic data."
[]

[invalid_sub_pp_name]
  type = RunException
  input = parent.i
  cli_args = Transfers/data/from_postprocessor=wrong
  expect_err = "Unknown postprocesssor name 'wrong' on sub-application 'sub'"

  requirement = "The system shall report an error if the supplied name is invalid when attempting to
                  transfer a single value from a sub-application."
[]

[abort]
  type = RunException
  input = cartesian_diverge.i
  cli_args = 'MultiApps/sub/ignore_solve_not_converge=false
                  sub0:Executioner/error_on_dtmin=true'
  expect_err = "Solve failed and timestep already at or below dtmin, cannot continue!"

  detail = "abort run, "
[]

[last_computed]
  type = CSVDiff
  input = cartesian_diverge.i
  csvdiff = 'cartesian_diverge_out_storage_0001.csv cartesian_diverge_out_storage_0002.csv'

  detail = "transfer last computed postprocessor value, "
  valgrind = 'none' # too slow
[]

[nan]
  type = CheckFiles
  input = cartesian_diverge.i
  cli_args = 'Transfers/data/keep_solve_fail_value=false
                  Outputs/file_base=cartesian_diverge_nan'
  check_files = 'cartesian_diverge_nan_storage_0001.csv'
  file_expect_out = 'data:avg\nnan\n0\.269307.*\n0\.269307.*\n0\.269307.*\n0\.269307.*\n'
  detail = "or transfer NaN."
  valgrind = 'none' # too slow
[]

[normal]
  type = CSVDiff
  input = parent_transient.i
  cli_args = MultiApps/runner/mode=normal
  csvdiff = 'parent_transient_out_storage_0000.csv parent_transient_out_storage_0001.csv parent_transient_out_storage_0002.csv parent_transient_out_storage_0003.csv'

  detail = 'with individual sub-applications,'
[]

[batch]
  type = CSVDiff
  input = parent_transient.i
  csvdiff = 'parent_transient_out_storage_0000.csv parent_transient_out_storage_0001.csv parent_transient_out_storage_0002.csv parent_transient_out_storage_0003.csv'
  prereq = transient/normal
  max_parallel = 5

  detail = 'with sub-applications batches using in-memory restore functionality.'
[]

[mode_error]
  type = RunException
  input = parent_transient.i
  cli_args = MultiApps/runner/mode=batch-reset
  expect_err = "The supplied mode, 'batch-reset', currently"
  requirement = "The SamplerTransientMultiApp object shall error if the 'batch-reset' mode is supplied."
[]

[normal]
  type = CSVDiff
  input = parent_transient.i
  cli_args = 'MultiApps/runner/mode=normal runner:Outputs/csv=true'
  csvdiff = 'parent_transient_out_runner0.csv
                 parent_transient_out_runner1.csv
                 parent_transient_out_runner2.csv
                 parent_transient_out_runner3.csv
                 parent_transient_out_runner4.csv'
  detail = 'individual sub-applications;'
[]

[batch]
  type = CSVDiff
  input = parent_transient.i
  cli_args = 'MultiApps/runner/mode=batch-restore runner:Outputs/csv=true'
  csvdiff = 'parent_transient_out_runner0.csv
                 parent_transient_out_runner1.csv
                 parent_transient_out_runner2.csv
                 parent_transient_out_runner3.csv
                 parent_transient_out_runner4.csv'
  detail = 'sub-application batches;'
[]

[normal_less_procs]
  type = CSVDiff
  input = main_transient.i
  allow_test_objects = true
  csvdiff = 'main_transient_out_vpp_0001.csv main_transient_out_vpp_0002.csv main_transient_out_vpp_0003.csv'
  max_parallel = 5
  detail = 'with less processors than rows in normal mode;'
[]

[normal_more_procs]
  type = CSVDiff
  input = main_transient.i
  allow_test_objects = true
  csvdiff = 'main_transient_out_vpp_0001.csv main_transient_out_vpp_0002.csv main_transient_out_vpp_0003.csv'
  min_parallel = 6
  detail = 'with more processors than rows in normal mode;'
[]

[normal_min_procs]
  type = CSVDiff
  input = main_transient.i
  cli_args = 'MultiApps/sub/min_procs_per_app=2 Samplers/sample/min_procs_per_row=2'
  allow_test_objects = true
  csvdiff = 'main_transient_out_vpp_0001.csv main_transient_out_vpp_0002.csv main_transient_out_vpp_0003.csv'
  min_parallel = 4
  detail = 'with specified minimum processors per app in normal mode;'
[]

[restore_less_procs]
  type = CSVDiff
  input = main_transient.i
  cli_args = 'MultiApps/sub/mode=batch-restore Controls/active="" Transfers/inactive=rep Reporters/check/reporters=vpp/pp:pp2'
  allow_test_objects = true
  csvdiff = 'main_transient_out_vpp_0001.csv main_transient_out_vpp_0002.csv main_transient_out_vpp_0003.csv'
  max_parallel = 5
  detail = 'with less processors than rows in batch-restore mode;'
[]

[restore_more_procs]
  type = CSVDiff
  input = main_transient.i
  cli_args = 'MultiApps/sub/mode=batch-restore Controls/active="" Transfers/inactive=rep Reporters/check/reporters=vpp/pp:pp2'
  allow_test_objects = true
  csvdiff = 'main_transient_out_vpp_0001.csv main_transient_out_vpp_0002.csv main_transient_out_vpp_0003.csv'
  min_parallel = 6
  detail = 'with more processors than rows in batch-restore mode;'
[]

[restore_min_procs]
  type = CSVDiff
  input = main_transient.i
  cli_args = 'MultiApps/sub/mode=batch-restore MultiApps/sub/min_procs_per_app=2 Samplers/sample/min_procs_per_row=2 Controls/active="" Transfers/inactive=rep Reporters/check/reporters=vpp/pp:pp2'
  allow_test_objects = true
  csvdiff = 'main_transient_out_vpp_0001.csv main_transient_out_vpp_0002.csv main_transient_out_vpp_0003.csv'
  min_parallel = 4
  detail = 'with specified minimum processors per app in batch-restore mode;'
[]

[error_min_procs]
  type = RunException
  input = main_transient.i
  cli_args = 'MultiApps/sub/min_procs_per_app=2 Samplers/sample/min_procs_per_row=1'
  allow_test_objects = true
  expect_err = "Sampler and multiapp communicator configuration inconsistent. Please ensure that 'MultiApps/sub/min\(max\)_procs_per_app' and 'Samplers/sample/min\(max\)_procs_per_row' are the same."
  min_parallel = 2
  detail = 'error when partitionings do not match;'
[]

[transient]
  type = CheckFiles
  input = parent_transient.i
  check_files = 'parent_transient_out_runner0.e parent_transient_out_runner1.e parent_transient_out_runner2.e parent_transient_out_runner3.e parent_transient_out_runner4.e'

  requirement = 'The system shall provide the ability to create a transient sub-application from the sample data generated from distributions.'
[]

[transient_cmdline_control]
  type = Exodiff
  input = parent_transient_cmd_control.i
  exodiff = 'parent_transient_cmd_control_out_runner0.e parent_transient_cmd_control_out_runner1.e parent_transient_cmd_control_out_runner2.e parent_transient_cmd_control_out_runner3.e parent_transient_cmd_control_out_runner4.e'

  requirement = 'The system shall provide the ability to set a transient sub-application command line parameters from a sample distribution.'

  issues = '#17486 #17548'
[]

[cmdline]
  type = CSVDiff
  input = main_transient.i
  csvdiff = 'main_transient_out_storage_0001.csv main_transient_out_storage_0002.csv main_transient_out_storage_0003.csv'
  detail = 'command line control;'
[]

[transfer_normal]
  type = CSVDiff
  input = main_transient.i
  cli_args = "Transfers/inactive='' Controls/active=''"
  csvdiff = 'main_transient_out_storage_0001.csv main_transient_out_storage_0002.csv main_transient_out_storage_0003.csv'
  detail = 'parameter transfer in normal mode;'
[]

[transfer_batch]
  type = CSVDiff
  input = main_transient.i
  cli_args = "MultiApps/sub/mode=batch-restore Transfers/inactive='' Controls/active=''"
  csvdiff = 'main_transient_out_storage_0001.csv main_transient_out_storage_0002.csv main_transient_out_storage_0003.csv'
  detail = 'parameter transfer in batch mode;'
[]

[control_missing]
  type = RunException
  input = parent_missing_control.i
  design = 'SamplerTransientMultiApp.md'
  expect_err = "The sub-application \(sub\) does not contain a SamplerReceiver control object."
  requirement = "The system shall produce an error if the sampler sub-application does not contain a Control object with the name 'stochastic'."
[]

[error]
  type = RunException
  input = conditional_main.i
  cli_args = MultiApps/runner/mode=normal
  expect_err = "Conditionally run sampler multiapp only works in batch modes."
  detail = 'error if running in normal mode.'
[]

[batch-restore]
  type = JSONDiff
  input = main.i
  allow_test_objects = true
  cli_args = 'MultiApps/runner/mode=batch-restore'
  jsondiff = main_out.json

  detail = "batch execution with memory-based restoring, and"
[]

[batch-reset]
  type = JSONDiff
  input = main.i
  allow_test_objects = true
  cli_args = 'MultiApps/runner/mode=batch-reset'
  jsondiff = main_out.json

  detail = "batch execution with reset-based restoring."
[]

[error]
  type = RunException
  input = main.i
  allow_test_objects = true
  expect_err = "The size of the sampler has changed; SamplerTransientMultiApp object do not support dynamic Sampler output."

  requirement = "The system shall report an error when the size of a sampler is altered an sub-applications are progressing with time with the main application."
[]

[constant]
  type = JSONDiff
  input = main.i
  jsondiff = main_constant_size_out.json
  allow_test_objects = true
  cli_args = "Samplers/dynamic/increment_rows=0 Outputs/file_base=main_constant_size_out"

  detail = "that remain constant in size and"
[]

[batch_reset]
  type = CSVDiff
  input = stm_batch.i
  cli_args = 'Outputs/file_base=stm_out MultiApps/main/mode=batch-reset'
  csvdiff = stm_out_storage_0001.csv
  detail = 'batch-reset multiapp mode;'
[]

[batch_restore]
  type = CSVDiff
  input = stm_batch.i
  cli_args = 'Outputs/file_base=stm_out MultiApps/main/mode=batch-restore'
  csvdiff = stm_out_storage_0001.csv
  detail = 'batch-restore multiapp mode;'
[]

[n1]
  type = CSVDiff
  input = parent.i
  csvdiff = 'parent_out_storage_0001.csv parent_out_data_0001.csv'
  cli_args = 'MultiApps/runner/mode=normal'
  max_parallel = 1

  detail = "on a single processor, "
[]

[n2]
  type = CSVDiff
  input = parent.i
  csvdiff = 'parent_out_storage_0001.csv parent_out_data_0001.csv'
  cli_args = 'MultiApps/runner/mode=normal'
  prereq = normal/n1
  min_parallel = 2
  max_parallel = 2

  detail = "on multiple processors, "
[]

[n3]
  type = CSVDiff
  input = parent.i
  csvdiff = 'parent_small_storage_0001.csv parent_small_data_0001.csv'
  cli_args = 'MultiApps/runner/mode=normal
                  Samplers/mc/num_rows=3
                  Outputs/file_base=parent_small'
  min_parallel = 4
  max_parallel = 4

  detail = "and on more processors than samples."
[]

[n2]
  type = CSVDiff
  input = parent.i
  csvdiff = 'parent_out_storage_0001.csv parent_out_data_0001.csv'
  min_parallel = 2
  max_parallel = 2
  prereq = batch/n1

  detail = "on multiple processors, and"
[]

[n2_restore]
  type = CSVDiff
  input = parent.i
  csvdiff = 'parent_out_storage_0001.csv parent_out_data_0001.csv'
  min_parallel = 2
  max_parallel = 2
  prereq = batch/n2
  cli_args = MultiApps/runner/mode=batch-restore

  detail = "on multiple processors using in-memory sub-application restore."
[]

[batch_two_subapps]
  requirement = "The system shall include the ability to transfer stochastic results for two sub apps."
  issues = '#17079'
  type = CSVDiff
  input = parent_2sub.i
  csvdiff = 'parent_2sub_out_data0_0001.csv parent_2sub_out_data1_0001.csv parent_2sub_out_storage0_0001.csv parent_2sub_out_storage1_0001.csv'
[]

[StochasticToolsTransfer_execute_on_error]
  type = RunException
  input = parent.i
  cli_args = Transfers/data/execute_on=INITIAL
  expect_err = "The 'execute_on' parameter for the 'data' transfer was set"
  requirement = "The 'StochasticToolsTransfer object shall error if the 'execute_on' parameter is defined when the corresponding MultiApp object is running in batch mode."
[]

[StochasticToolsTransfer_execute_on_check]
  type = RunException
  input = parent.i
  cli_args = "Transfers/data/execute_on=INITIAL MultiApps/runner/mode=normal"
  expect_err = "MultiAppTransfer execute_on flags do not match associated from_multi_app execute_on flags"
  requirement = "The 'StochasticToolsTransfer' object shall error if the 'execute_on' parameter does not match the corresponding MultiApp object is running in normal mode."
[]

[direction_error]
  type = RunException
  input = parent.i
  cli_args = 'Transfers/data/to_multi_app=runner'
  expect_err = 'Transfers between multiapp are not currently supported for this transfer type'
  requirement = 'The system shall report a reasonable error if parameters for a trasnfer between multiapps are provided to stochastics transfer, which do not support this currently'
[]

[monte_carlo]
  type = CSVDiff
  input = monte_carlo.i
  csvdiff = 'monte_carlo_out_sub0.csv monte_carlo_out_sub1.csv monte_carlo_out_sub2.csv monte_carlo_out_sub3.csv monte_carlo_out_sub4.csv'
  requirement = "The system shall support the creation of a sub-application for each row of the stochastic data."
[]

[multiapp_type]
  type = RunException
  input = parent_multiapp_type_error.i
  expect_err = "The 'multi_app' parameter must provide either a 'SamplerTransientMultiApp' or 'SamplerFullSolveMultiApp' object."
  design = 'SamplerParameterTransfer.md'
  requirement = "The system shall produce an error if neither a 'SamplerTransientMultiApp' nor `SamplerFullSolveMultiApp` is provided in SamplerParameterTransfer."
[]

[num_parameters_wrong]
  type = RunException
  input = parent_num_parameters_wrong.i
  expect_err = "Number of command line arguments does not match number of sampler columns."
  design = 'SamplerParameterTransfer.md'
  requirement = "The system shall produce an error if supplied vector of real values is not sized correctly within the SamplerParameterTransfer object."
[]

[not_vector]
  type = RunException
  input = parent_not_vector.i
  expect_err = "The Real parameter '[a-z,A-Z,/]+' expects a single value, but the vector supplying its values has a size of \d+\."
  design = 'SamplerParameterTransfer.md'
  requirement = "The system shall produce an error if a vector of values is supplied to a scalar parameter."
[]

[sampler_mismatch]
  type = RunException
  input = parent_transfer_wrong_sampler.i
  expect_err = "The supplied 'multi_app' must have the same Sampler object as this Transfer."
  design = 'SamplerParameterTransfer.md'
  requirement = "The system shall produce an error if sampling method differs between the sub-application and the associated sub-application data transfer."
[]

[monte_carlo]
  type = CSVDiff
  input = monte_carlo.i
  csvdiff = 'monte_carlo_out_sub0.csv monte_carlo_out_sub1.csv monte_carlo_out_sub2.csv monte_carlo_out_sub3.csv monte_carlo_out_sub4.csv'

  detail = 'using a Monte Carlo and'
[]

[sobol]
  type = CSVDiff
  input = sobol.i
  csvdiff = 'sobol_out_sub00.csv sobol_out_sub01.csv sobol_out_sub02.csv sobol_out_sub03.csv sobol_out_sub04.csv sobol_out_sub05.csv sobol_out_sub06.csv sobol_out_sub07.csv sobol_out_sub08.csv sobol_out_sub09.csv sobol_out_sub10.csv sobol_out_sub11.csv sobol_out_sub12.csv sobol_out_sub13.csv sobol_out_sub14.csv sobol_out_sub15.csv sobol_out_sub16.csv sobol_out_sub17.csv'

  detail = 'Sobol sampling scheme.'
[]

[monte_carlo]
  type = CSVDiff
  input = parent.i
  csvdiff = 'parent_out_sub0.csv parent_out_sub1.csv parent_out_sub2.csv parent_out_sub3.csv parent_out_sub4.csv'
  requirement = "The system shall be capable of transferring vector data to sub-applications for each row of the stochastic data."
[]

[invalid_name]
  type = RunException
  input = parent.i
  cli_args = "Transfers/sub/parameters='foo bar fah lala' :Outputs/csv=false"
  expect_err = "The desired parameter '\w+' was not located for the 'stochastic' object, it either does not exist or has not been declared as controllable."
  detail = "if the vector parameter does not exist;"
[]

[extra_data]
  type = RunException
  input = parent.i
  cli_args = "Transfers/sub/parameters='BCs/left/value' :Outputs/csv=false"
  expect_err = "Number of command line arguments does not match number of sampler columns."
  detail = "if the sub-application does not consume all of the supplied data;"
[]

[sobol]
  type = CSVDiff
  input = sobol.i
  csvdiff = 'sobol_out_sub00.csv sobol_out_sub01.csv sobol_out_sub02.csv sobol_out_sub03.csv sobol_out_sub04.csv sobol_out_sub05.csv sobol_out_sub06.csv sobol_out_sub07.csv sobol_out_sub08.csv sobol_out_sub09.csv sobol_out_sub10.csv sobol_out_sub11.csv sobol_out_sub12.csv sobol_out_sub13.csv sobol_out_sub14.csv sobol_out_sub15.csv sobol_out_sub16.csv sobol_out_sub17.csv'

  requirement = "The system shall support the creation of a sub-application for each row sampled data generated from a Sobol scheme."
[]

[SingleProc_SingleRow_Ufunction]
  type = 'JSONDiff'
  input = main_adam.i
  jsondiff = 'single_proc_single_row_ufunction.json'
  detail = 'a single row in the sampler using the U learning function;'
[]

[MultipleProc_MultipleRow_Ufunction]
  type = 'JSONDiff'
  input = main_adam.i
  min_parallel = 2
  jsondiff = 'multiple_proc_multiple_row_ufunction.json'
  cli_args = "Samplers/mc/num_batch=2 Outputs/file_base='multiple_proc_multiple_row_ufunction'"
  detail = 'multiple rows in the sampler using the U learning function and automatically increase the number of steps when re-training is needed;'
[]

[SingleProc_SingleRow_Ufunction_retraining]
  type = 'JSONDiff'
  input = main_adam.i
  jsondiff = 'single_proc_single_row_ufunction_retraining.json'
  cli_args = "Reporters/conditional/learning_function_threshold=5.0 Outputs/file_base='single_proc_single_row_ufunction_retraining'"
  detail = 'a single row in the sampler using the U learning function and automatically increase the number of steps when re-training is needed;'
[]

[COV_squared_exp]
  type = 'JSONDiff'
  input = main_adam.i
  jsondiff = 'cov_squared_exp.json'
  cli_args = "Reporters/conditional/learning_function='COV' Reporters/conditional/learning_function_threshold=0.025 Outputs/file_base='cov_squared_exp'"
  detail = 'a single row in the sampler using the COV learning function and automatically increase the number of steps when re-training is needed;'
[]

[SingleProc_SingleRow_Ufunction]
  type = 'JSONDiff'
  input = main_adam.i
  jsondiff = 'single_proc_single_row_ufunction.json'
  detail = 'a single row in the sampler using the U learning function;'
[]

[MultipleProc_MultipleRow_Ufunction]
  type = 'JSONDiff'
  input = main_adam.i
  min_parallel = 2
  jsondiff = 'multiple_proc_multiple_row_ufunction.json'
  cli_args = "Samplers/mc/num_batch=2 Outputs/file_base='multiple_proc_multiple_row_ufunction'"
  detail = 'multiple rows in the sampler using the U learning function and automatically increase the number of steps when re-training is needed;'
[]

[SingleProc_SingleRow_Ufunction_retraining]
  type = 'JSONDiff'
  input = main_adam.i
  jsondiff = 'single_proc_single_row_ufunction_retraining.json'
  cli_args = "Reporters/conditional/learning_function_threshold=5.0 Outputs/file_base='single_proc_single_row_ufunction_retraining'"
  detail = 'a single row in the sampler using the U learning function and automatically increase the number of steps when re-training is needed;'
[]

[COV_squared_exp]
  type = 'JSONDiff'
  input = main_adam.i
  jsondiff = 'cov_squared_exp.json'
  cli_args = "Reporters/conditional/learning_function='COV' Reporters/conditional/learning_function_threshold=0.025 Outputs/file_base='cov_squared_exp'"
  detail = 'a single row in the sampler using the COV learning function and automatically increase the number of steps when re-training is needed;'
[]

[SingleProc_SingleRow_Ufunction]
  type = 'JSONDiff'
  input = main_adam.i
  jsondiff = 'single_proc_single_row_ufunction.json'
  detail = 'a single row in the sampler using the U learning function;'
[]

[MultipleProc_MultipleRow_Ufunction]
  type = 'JSONDiff'
  input = main_adam.i
  min_parallel = 2
  jsondiff = 'multiple_proc_multiple_row_ufunction.json'
  cli_args = "Samplers/mc/num_batch=2 Outputs/file_base='multiple_proc_multiple_row_ufunction'"
  detail = 'multiple rows in the sampler using the U learning function and automatically increase the number of steps when re-training is needed;'
[]

[SingleProc_SingleRow_Ufunction_retraining]
  type = 'JSONDiff'
  input = main_adam.i
  jsondiff = 'single_proc_single_row_ufunction_retraining.json'
  cli_args = "Reporters/conditional/learning_function_threshold=5.0 Outputs/file_base='single_proc_single_row_ufunction_retraining'"
  detail = 'a single row in the sampler using the U learning function and automatically increase the number of steps when re-training is needed;'
[]

[COV_squared_exp]
  type = 'JSONDiff'
  input = main_adam.i
  jsondiff = 'cov_squared_exp.json'
  cli_args = "Reporters/conditional/learning_function='COV' Reporters/conditional/learning_function_threshold=0.025 Outputs/file_base='cov_squared_exp'"
  detail = 'a single row in the sampler using the COV learning function and automatically increase the number of steps when re-training is needed;'
[]

[SingleProc_SingleRow_Ufunction]
  type = 'JSONDiff'
  input = main_adam.i
  min_parallel = 1
  max_parallel = 1
  jsondiff = 'single_proc_single_row_ufunction.json'
  detail = 'a single row in the sampler using the U learning function;'
[]

[MultipleProc_MultipleRow_Ufunction]
  type = 'JSONDiff'
  input = main_adam.i
  min_parallel = 2
  max_parallel = 2
  jsondiff = 'multiple_proc_multiple_row_ufunction.json'
  cli_args = "Samplers/mc/num_batch=2 Outputs/file_base='multiple_proc_multiple_row_ufunction'"
  detail = 'multiple rows in the sampler using the U learning function;'
[]

[error]
  type = RunException
  input = statistics_reporter_error.i
  expect_err = "The following reporter value\(s\) do not have a type supported by the StatisticsReporter:\n\s+const/const"

  detail = "and error if the supplied type is not supported."
[]

[replicated]
  type = JSONDiff
  input = bca_distributed.i
  jsondiff = bca_distributed_out.json
  cli_args = "VectorPostprocessors/data/parallel_type=replicated"
  min_parallel = 3
  max_parallel = 3
  allow_test_objects = true

  detail = "using data that is replicated and"
[]

[distributed]
  type = JSONDiff
  input = bca_distributed.i
  jsondiff = bca_distributed_out.json
  min_parallel = 3
  max_parallel = 3
  allow_test_objects = true

  detail = "using data that is distributed."
[]

[no_ci_levels]
  type = RunException
  input = error.i
  cli_args = "Reporters/stats/ci_levels=''"
  expect_err = "If the 'ci_method' parameter is supplied then the 'ci_levels' must also be supplied with values in \(0, 1\)"

  detail = "the confidence level intervals are omitted;"
[]

[ci_levels_zero]
  type = RunException
  input = error.i
  cli_args = "Reporters/stats/ci_levels='0'"
  expect_err = "The supplied levels must be greater than zero"

  detail = "the confidence level intervals are less than or equal to zero;"
[]

[ci_levels_one]
  type = RunException
  input = error.i
  cli_args = "Reporters/stats/ci_levels='1'"
  expect_err = "The supplied levels must be less than 1\.0"

  detail = "the confidence level intervals are greater than or equal to one;"
[]

[no_input]
  type = RunException
  input = error.i
  cli_args = "Reporters/stats/vectorpostprocessors=''"
  expect_err = "The 'vectorpostprocessors' and/or 'reporters' parameters must be defined and non-empty"

  detail = "input is not provided."
[]

[replicated]
  type = JSONDiff
  input = percentile_distributed.i
  jsondiff = percentile_distributed_out.json
  cli_args = "VectorPostprocessors/data/parallel_type=replicated"
  min_parallel = 3
  max_parallel = 3
  allow_test_objects = true

  detail = "using data that is replicated or"
[]

[distributed]
  type = JSONDiff
  input = percentile_distributed.i
  jsondiff = percentile_distributed_out.json
  prereq = percentile/replicated
  min_parallel = 3
  max_parallel = 3
  allow_test_objects = true

  detail = "distributed across processors."
[]

[distributed]
  type = 'JSONDiff'
  input = 'statistics_distributed.i'
  jsondiff = 'statistics_distributed_out.json'
  min_parallel = 3
  max_parallel = 3
  allow_test_objects = true

  detail = 'distributed.'
[]

[reporter]
  type = JSONDiff
  input = statistics_main.i
  jsondiff = statistics_main_out.json
  requirement = 'The system shall be capable of computing the statistics from vector reporter values.'
[]

[distributed]
  type = JSONDiff
  input = dp_main.i
  jsondiff = dp_main_out.json
  allow_test_objects = true
  detail = 'on distributed data sets;'
  cli_args = 'Samplers/directperturbation/perturbation_method=forward_difference'
[]

[root]
  type = JSONDiff
  input = dp_main.i
  jsondiff = dp_main_out.json
  allow_test_objects = true
  detail = 'on root processor;'
  cli_args = 'Reporters/storage/parallel_type=ROOT Samplers/directperturbation/perturbation_method=forward_difference'
[]

[relative_sensitivity]
  type = JSONDiff
  input = dp_main.i
  cli_args = 'Reporters/sensitivities/relative_sensitivity=true Outputs/file_base=relative_sens'
  jsondiff = relative_sens.json
  allow_test_objects = true
  requirement = 'The system shall support the ability to compute direct perturbation-based relative sensitivity values.'
[]

[from_parallelstorage]
  type = JSONDiff
  requirement = 'The system should be able to map the solutions in a snapshot container into a latent space.'
  input = map_main.i
  jsondiff = 'map_training_data.json'
  min_parallel = 2
  max_parallel = 2
  petsc_version = ">=3.14"
[]

[from_solutions]
  # Note: this test should result in the same reporter values as the one above. Due to reporter naming difference, we
  # needed two different gold files. If something causes mismatch between the values in the two gold files, look for bugs.
  type = JSONDiff
  requirement = 'The system should be able to extract and map variables in nonlinear system into a latent (low-dimensional) space.'
  input = load_main.i
  cli_args = "worker:VariableMappings/inactive='' worker:Reporters/inactive=''"
  jsondiff = 'map_variable.json'
  min_parallel = 2
  max_parallel = 2
  prereq = from_parallelstorage
  petsc_version = ">=3.14"
[]

[replicated]
  type = JSONDiff
  input = morris.i
  cli_args = 'VectorPostprocessors/gfun/parallel_type=REPLICATED'
  jsondiff = morris_out.json
  allow_test_objects = true
  detail = 'replicated data;'
[]

[root]
  type = JSONDiff
  input = morris_main.i
  cli_args = 'Reporters/storage/parallel_type=ROOT'
  jsondiff = morris_main_out.json
  allow_test_objects = true
  method = 'opt'
  detail = 'on root processor;'
[]

[print-p2]
  type = JSONDiff
  requirement = 'The system should be able to print serialized snapshots in a distributed fashion in a json format.'
  input = parallel_storage_main.i
  jsondiff = 'parallel_storage_main_out.json parallel_storage_main_out.json.1'

  min_parallel = 2
  max_parallel = 2
[]

[sobol]
  type = JSONDiff
  input = sobol.i
  jsondiff = sobol_out.json
  allow_test_objects = true

  requirement = "The system shall support the ability to compute first, second, and total-effect Sobol sensitivity indices with a reporter."
[]

[sobol_vec]
  type = JSONDiff
  input = sobol_main.i
  jsondiff = sobol_main_out.json
  allow_test_objects = true

  requirement = "The system shall support the ability to compute Sobol sensitivity indices for vector-type data."
  method = 'opt'
[]

[parallel]
  type = JSONDiff
  input = sample_only.i
  cli_args = 'Reporters/matrix/parallel_type=distributed Outputs/file_base=sample_only_dist'
  min_parallel = 3
  max_parallel = 3
  jsondiff = 'sample_only_dist.json sample_only_dist.json.1 sample_only_dist.json.2'
  detail = 'distributed output;'
[]

[names]
  type = JSONDiff
  input = sample_only.i
  cli_args = 'Reporters/matrix/sampler_column_names=\'foo bar fah lala\' Outputs/file_base=sample_only_names'
  jsondiff = sample_only_names.json
  detail = 'specified column names;'
[]

[simulation]
  type = CSVDiff
  input = stochastic_matrix.i
  csvdiff = stochastic_matrix_out_matrix_0001.csv
  requirement = 'The system shall be able to output samples from a sampler along side stochastic simulation results.'
[]

[wrong_num_names]
  type = RunException
  input = sample_only.i
  cli_args = 'Reporters/matrix/sampler_column_names=foobar'
  expect_err = 'The number of column names specified \(1\) does not match the number of sampler columns \(4\)\.'
  detail = 'inputting the wrong number of column names;'
[]

[wrong_sampler]
  type = RunException
  input = stochastic_matrix.i
  cli_args = 'Samplers/wrong/type=InputMatrix Samplers/wrong/matrix=\'1 2 3 4\' Reporters/matrix/sampler=wrong'
  expect_err = 'Attempting to create a stochastic vector with a different sampler \(sample\) than the one specified at input \(wrong\)\.'
  detail = 'using a different sampler than the transfer\'s;'
[]

[ais]
  requirement = 'The system shall include an Adaptive Importance Sampling method for sampling distribution data.'
  type = 'JSONDiff'
  skip_keys = 'number_of_parts part'
  input = 'ais.i'
  jsondiff = 'ais_out.json'
  valgrind = none
[]

[ais_error1]
  type = RunException
  input = 'ais_error1.i'
  detail = 'the selected sampler type is not of an adaptive type when performing adaptive decision;'
  expect_err = "The selected sampler is not an adaptive sampler."
[]

[pss1]
  requirement = 'The system shall include an Parallel Subset Simulation method for sampling distribution data.'
  type = 'JSONDiff'
  skip_keys = 'number_of_parts part'
  input = 'pss.i'
  jsondiff = 'pss_out.json'
  valgrind = none
[]

[pss_error1]
  type = RunException
  input = 'pss_error1.i'
  detail = 'the selected sampler type is not of an adaptive type. '
  expect_err = "The selected sampler is not an adaptive sampler."
[]

[no_triplet]
  type = RunException
  input = grid.i
  cli_args = "Samplers/sample/linear_space_items='1 1'"
  expect_err = "The number of numeric items must be divisible by 3; min, max, divisions for each item are required."
  match_literal = True
  detail = 'the specified grid does not form a triplet,'
[]

[wrong_num_values]
  type = RunException
  input = grid.i
  cli_args = "Samplers/sample/nominal_values='1 1'"
  expect_err = "The number of values specified must match the number of triplets in 'linear_space_items'."
  match_literal = True
  detail = 'the number of nominal values specified does not match the number of sample columns,'
[]

[non_int]
  type = RunException
  input = grid.i
  cli_args = "Samplers/sample/linear_space_items='1 1 0.1 1 1 1 1 1 1'"
  expect_err = "The third entry for each item must be an integer; it provides the number of entries in the resulting item vector."
  match_literal = True
  detail = 'the number of grid points is not an integer, or'
[]

[negative]
  type = RunException
  input = grid.i
  cli_args = "Samplers/sample/linear_space_items='1 1 -1 1 1 1 1 1 1'"
  expect_err = "The third entry for each item must be positive; it provides the number of entries in the resulting item vector."
  match_literal = True
  detail = 'the number of grid points is negative.'
[]

[sampling_test_global]
  type = 'CSVDiff'
  input = 'csv_sampler.i'
  csvdiff = 'csv_sampler_out_data_0000.csv csv_sampler_out_data_0001.csv'
  prereq = csv_sampler/sampling_test
  cli_args = VectorPostprocessors/data/sampler_method=get_global_samples

  detail = 'replicated across processors, and '
[]

[sampling_test_distributed]
  type = 'CSVDiff'
  input = 'csv_sampler.i'
  csvdiff = 'csv_sampler_distributed_out_data_0000.csv.0 csv_sampler_distributed_out_data_0000.csv.1 csv_sampler_distributed_out_data_0001.csv.0 csv_sampler_distributed_out_data_0001.csv.1'
  cli_args = 'VectorPostprocessors/data/parallel_type=DISTRIBUTED Outputs/file_base=csv_sampler_distributed_out'
  min_parallel = 2
  max_parallel = 2
  detail = 'distributed across processors with the output also distributed.'
[]

[csv_sampler_indices]
  requirement = "The CSV Sampler shall sample from a CSV file when column indices are provided."

  type = 'CSVDiff'
  input = 'csv_sampler_indices.i'
  csvdiff = 'csv_sampler_indices_out_data_0000.csv csv_sampler_indices_out_data_0001.csv'
[]

[csv_sampler_names]
  requirement = "The CSV Sampler shall sample from a CSV file when column names are provided."

  type = 'CSVDiff'
  input = 'csv_sampler_names.i'
  csvdiff = 'csv_sampler_indices_out_data_0000.csv csv_sampler_indices_out_data_0001.csv'
  cli_args = Outputs/file_base='csv_sampler_indices_out'
[]

[parallel]
  type = CSVDiff
  input = directperturbation.i
  cli_args = 'VectorPostprocessors/data/parallel_type=DISTRIBUTED Outputs/file_base=dp_cd_3'
  min_parallel = 3
  max_parallel = 3
  csvdiff = 'dp_cd_3_data_0001.csv.0 dp_cd_3_data_0001.csv.1 dp_cd_3_data_0001.csv.2'
  detail = "with parallel output;"
[]

[serial]
  type = CSVDiff
  input = directperturbation.i
  csvdiff = 'dp_fd_data_0001.csv'
  detail = "with replicated output;"
  cli_args = 'Outputs/file_base=dp_fd Samplers/directperturbation/perturbation_method=forward_difference'
[]

[parallel]
  type = CSVDiff
  input = directperturbation.i
  cli_args = 'Samplers/directperturbation/perturbation_method=forward_difference VectorPostprocessors/data/parallel_type=DISTRIBUTED Outputs/file_base=dp_fd_3'
  min_parallel = 3
  max_parallel = 3
  csvdiff = 'dp_fd_3_data_0001.csv.0 dp_fd_3_data_0001.csv.1 dp_fd_3_data_0001.csv.2'
  detail = "with parallel output;"
[]

[initial]
  type = CSVDiff
  input = initial.i
  csvdiff = 'initial_out_data_0000.csv initial_out_data_0001.csv initial_out_data_0002.csv initial_out_data_0003.csv'

  requirement = 'The system shall support the creation of data sampled from distribution during the initial setup of a simulation.'
[]

[global]
  type = CSVDiff
  input = 'latin_hypercube.i'
  cli_args = 'VectorPostprocessors/data/sampler_method=get_global_samples'
  csvdiff = 'latin_hypercube_out_data_0000.csv latin_hypercube_out_data_0001.csv'

  detail = "using global matrix,"
[]

[local]
  type = CSVDiff
  input = 'latin_hypercube.i'
  cli_args = 'VectorPostprocessors/data/sampler_method=get_local_samples'
  csvdiff = 'latin_hypercube_out_data_0000.csv latin_hypercube_out_data_0001.csv'

  detail = "a local matrix,"
[]

[row]
  type = CSVDiff
  input = 'latin_hypercube.i'
  cli_args = 'VectorPostprocessors/data/sampler_method=get_next_local_row'
  csvdiff = 'latin_hypercube_out_data_0000.csv latin_hypercube_out_data_0001.csv'

  detail = "or row-by-row."
[]

[global]
  type = CSVDiff
  input = 'latin_hypercube.i'
  cli_args = 'VectorPostprocessors/data/sampler_method=get_global_samples Samplers/sample/num_rows=2
                  Outputs/file_base=latin_hypercube_small'
  csvdiff = 'latin_hypercube_small_data_0000.csv latin_hypercube_small_data_0001.csv'
  min_parallel = 4

  detail = "using global matrix,"
[]

[local]
  type = CSVDiff
  input = 'latin_hypercube.i'
  cli_args = 'VectorPostprocessors/data/sampler_method=get_local_samples Samplers/sample/num_rows=2
                  Outputs/file_base=latin_hypercube_small'
  csvdiff = 'latin_hypercube_small_data_0000.csv latin_hypercube_small_data_0001.csv'
  min_parallel = 4

  detail = "a local matrix,"
[]

[row]
  type = CSVDiff
  input = 'latin_hypercube.i'
  cli_args = 'VectorPostprocessors/data/sampler_method=get_next_local_row Samplers/sample/num_rows=2
                  Outputs/file_base=latin_hypercube_small'
  csvdiff = 'latin_hypercube_small_data_0000.csv latin_hypercube_small_data_0001.csv'
  min_parallel = 4

  detail = "or row-by-row."
[]

[visualize]
  type = CheckFiles
  command = "./latin_hypercube.py"
  check_files = latin_hypercube.pdf
  required_python_packages = 'pandas numpy matplotlib'
  valgrind = NONE

  requirement = "The system shall include a utility that visually displays results of plotting Latin Hypercube test."
[]

[pmcmc_base_bounds]
  type = 'JSONDiff'
  input = main_base.i
  allow_test_objects = true
  jsondiff = 'mcmc_base_bounds.json'
  cli_args = "Samplers/sample/lower_bound='-2.0 -2.0' Samplers/sample/upper_bound='2.0 2.0' Outputs/file_base='mcmc_base_bounds'"
  min_parallel = 4
  max_parallel = 4
  detail = 'using the base MCMC class with bounds specified; '
[]

[imh_5prop]
  type = 'JSONDiff'
  input = main_imh.i
  allow_test_objects = true
  jsondiff = 'imh_5prop.json'
  min_parallel = 4
  max_parallel = 4
  detail = 'using the independent Metropolis-Hastings class; '
[]

[des_5prop]
  type = 'JSONDiff'
  input = main_des.i
  allow_test_objects = true
  jsondiff = 'des_5prop.json'
  min_parallel = 4
  max_parallel = 4
  detail = 'using the differential evolution sampler class; '
[]

[des_5prop_var]
  type = 'JSONDiff'
  input = main_des_var.i
  allow_test_objects = true
  jsondiff = 'des_5prop_var.json'
  min_parallel = 4
  max_parallel = 4
  detail = 'using the differential evolution sampler class with variance inferred; '
[]

[pmcmc_base_bounds1]
  type = RunException
  input = main_base.i
  cli_args = "Samplers/sample/lower_bound='-2.0 -2.0'"
  detail = 'only the lower bounds specified; '
  expect_err = "Both lower and upper bounds should be specified."
[]

[pmcmc_base_bounds2]
  type = RunException
  input = main_base.i
  cli_args = "Samplers/sample/lower_bound='-2.0 -2.0' Samplers/sample/upper_bound='2.0'"
  detail = 'the specified bounds are not of the same size; '
  expect_err = "Lower and upper bounds should be of the same size."
[]

[pmcmc_base_sizes]
  type = RunException
  input = main_base.i
  cli_args = "Samplers/sample/initial_values='0.1'"
  detail = 'the priors, bounds, and initial values are not of the same size; '
  expect_err = "The priors and initial values should be of the same size."
[]

[imh_std_sizes]
  type = RunException
  input = main_imh.i
  cli_args = "Samplers/sample/std_prop='0.05'"
  detail = 'the proposal stds, initial values, and priors are not of the same size; '
  expect_err = "The number of proposal stds, initial values, and priors should be the same."
[]

[des_scale_sizes]
  type = RunException
  input = main_des.i
  cli_args = "Samplers/sample/scales='1.0'"
  detail = 'the number of scales provided do not match the number of tunable params; '
  expect_err = "The number of scales provided should match the number of tunable params."
[]

[uniform]
  type = 'CSVDiff'
  input = 'monte_carlo_uniform.i'
  csvdiff = 'monte_carlo_uniform_out_data_0000.csv monte_carlo_uniform_out_data_0001.csv'

  detail = 'a uniform distribution distributed across processors,'
[]

[uniform_global]
  type = 'CSVDiff'
  input = 'monte_carlo_uniform.i'
  csvdiff = 'monte_carlo_uniform_out_data_0000.csv monte_carlo_uniform_out_data_0001.csv'
  prereq = monte_carlo/uniform
  cli_args = VectorPostprocessors/data/sampler_method=get_global_samples

  detail = 'a uniform distribution replicated across processors,'
[]

[uniform_distributed]
  type = 'CSVDiff'
  input = 'monte_carlo_uniform.i'
  csvdiff = 'monte_carlo_uniform_distributed_out_data_0000.csv.0 monte_carlo_uniform_distributed_out_data_0000.csv.1 monte_carlo_uniform_distributed_out_data_0001.csv.0 monte_carlo_uniform_distributed_out_data_0001.csv.1'
  cli_args = 'VectorPostprocessors/data/parallel_type=DISTRIBUTED Outputs/file_base=monte_carlo_uniform_distributed_out'
  min_parallel = 2
  max_parallel = 2
  detail = 'a uniform distribution distributed across processors (output is also distributed),'
[]

[weibull]
  type = 'CSVDiff'
  input = 'monte_carlo_weibull.i'
  csvdiff = 'monte_carlo_weibull_out_data_0000.csv monte_carlo_weibull_out_data_0001.csv'

  detail = 'a Wiebull distribution distributed across processors, and'
[]

[weibull_global]
  type = 'CSVDiff'
  input = 'monte_carlo_weibull.i'
  csvdiff = 'monte_carlo_weibull_out_data_0000.csv monte_carlo_weibull_out_data_0001.csv'
  prereq = monte_carlo/weibull
  cli_args = VectorPostprocessors/data/sampler_method=get_global_samples

  detail = 'a Weibull distribution replicated across processors.'
[]

[monte_carlo]
  type = CSVDiff
  input = monte_carlo.i
  csvdiff = 'monte_carlo_out_sub0.csv monte_carlo_out_sub1.csv monte_carlo_out_sub2.csv monte_carlo_out_sub3.csv monte_carlo_out_sub4.csv'
  requirement = "The system shall support the creation of a sub-application for each row of the stochastic data."
[]

[parallel]
  type = CSVDiff
  input = morris.i
  cli_args = 'VectorPostprocessors/data/parallel_type=DISTRIBUTED Outputs/file_base=morris3'
  min_parallel = 3
  max_parallel = 3
  csvdiff = 'morris3_data_0000.csv.0 morris3_data_0000.csv.1 morris3_data_0000.csv.2 morris3_data_0001.csv.0 morris3_data_0001.csv.1 morris3_data_0001.csv.2'
  detail = "with parallel output;"
[]

[parallel_more]
  type = CSVDiff
  input = morris.i
  cli_args = 'VectorPostprocessors/data/parallel_type=DISTRIBUTED Outputs/file_base=morris5'
  min_parallel = 5
  max_parallel = 5
  csvdiff = 'morris5_data_0000.csv.0 morris5_data_0000.csv.1 morris5_data_0000.csv.2 morris5_data_0000.csv.3 morris5_data_0000.csv.4
                 morris5_data_0001.csv.0 morris5_data_0001.csv.1 morris5_data_0001.csv.2 morris5_data_0001.csv.3 morris5_data_0001.csv.4'
  detail = "with more processors than trajectories;"
[]

[error]
  type = RunException
  input = nested_monte_carlo.i
  cli_args = "Samplers/nested_mc/num_rows='10 3'"
  expect_err = "There must be a set of distributions for each loop defined by 'num_rows'"
  requirement = 'The system shall report an error when the number of nested Monte Carlo loops does not match the number of sets of distributions.'
[]

[no_resample]
  type = CSVDiff
  input = sobol.i
  cli_args = 'Samplers/sobol/resample=false Outputs/file_base=sobol_no_resample_out'
  csvdiff = 'sobol_no_resample_out_data_0000.csv'

  detail = "without the re-sampling matrix."
[]

[row_mismatch]
  type = RunException
  input = sobol.i
  cli_args = "Samplers/sample/num_rows=42"
  expect_err = "The supplied Sampler objects must have the same number of rows"

  detail = "with differing number of rows;"
[]

[col_mismatch]
  type = RunException
  input = sobol.i
  cli_args = "Samplers/sample/distributions='d0 d1'"
  expect_err = "The supplied Sampler objects must have the same number of columns"

  detail = "with differing number of columns; and"
[]

[same_matrix]
  type = RunException
  input = sobol.i
  cli_args = "Samplers/sobol/sampler_a=resample"
  expect_err = "The supplied sampler matrices must not be the same"

  detail = "if the matrices are the same."
[]

[sobol]
  type = CSVDiff
  input = sobol.i
  csvdiff = 'sobol_out_sub00.csv sobol_out_sub01.csv sobol_out_sub02.csv sobol_out_sub03.csv sobol_out_sub04.csv sobol_out_sub05.csv sobol_out_sub06.csv sobol_out_sub07.csv sobol_out_sub08.csv sobol_out_sub09.csv sobol_out_sub10.csv sobol_out_sub11.csv sobol_out_sub12.csv sobol_out_sub13.csv sobol_out_sub14.csv sobol_out_sub15.csv sobol_out_sub16.csv sobol_out_sub17.csv'

  requirement = "The system shall support the creation of a sub-application for each row sampled data generated from a Sobol scheme."
[]

[VectorPostprocessors_sampler]
  requirement = 'The system shall be able to perform stochastic sampling based on global vector data.'
  type = 'CSVDiff'
  input = 'vectorpostprocessor.i'
  csvdiff = 'vectorpostprocessor_out_data_0002.csv'
[]

[cv_real]
  type = JSONDiff
  input = train_and_cv.i
  allow_test_objects = true
  cli_args = "Trainers/train/cv_surrogate='surrogate'"
  jsondiff = 'train_and_cv_out.json'
  detail = 'for a 1-dimensional polynomial regression surrogate with Real'
[]

[surr-error]
  type = RunException
  input = train_and_cv.i
  allow_test_objects = true
  expect_err = "To perform cross-validation, the option cv_surrogate needs to be specified to provide a Surrogate object for training and evaluation."
  detail = ' and throw an error if a SurrogateModel is not provided.'
[]

[evaluate]
  type = JSONDiff
  input = evaluate.i
  jsondiff = evaluate_out.json
  prereq = surrogate/train # Creates data files that this test uses
  allow_warnings = true
  detail = "evaluated separately (with replicated data);"
[]

[train_distributed]
  type = CheckFiles
  input = train.i
  cli_args = Reporters/storage/parallel_type=DISTRIBUTED
  check_files = 'train_out_poly_chaos.rd/data'
  prereq = surrogate/evaluate
  allow_test_objects = true

  detail = "trained with distributed stochastic data and"
[]

[evaluate_distributed]
  type = JSONDiff
  input = evaluate.i
  jsondiff = evaluate_out.json
  prereq = surrogate/train_distributed # Creates data files that this test uses
  allow_warnings = true
  detail = "evaluated separately (with distributed data);"
[]

[train_three_ranks]
  type = CheckFiles
  input = train.i
  cli_args = Reporters/storage/parallel_type=DISTRIBUTED
  check_files = 'train_out_poly_chaos.rd/data'
  prereq = surrogate/evaluate_distributed
  allow_test_objects = true
  min_parallel = 3

  detail = "trained with distributed stochastic data;"
[]

[evaluate_two_ranks]
  type = JSONDiff
  input = evaluate.i
  jsondiff = evaluate_out.json
  prereq = surrogate/train_three_ranks # Creates data files that this test uses
  max_parallel = 2

  detail = "evaluated separately with a different number of processors;"
[]

[evaluate_and_train]
  type = JSONDiff
  input = train_and_evaluate.i
  cli_args = 'Outputs/file_base=evaluate_out'
  jsondiff = evaluate_out.json
  allow_test_objects = true

  detail = "and be trained and evaluated in memory with a single input file."
[]

[GP_training]
  type = CheckFiles
  input = GP_squared_exponential_training.i
  check_files = 'gauss_process_training_GP_avg_trainer.rd/data'
  allow_test_objects = true
  detail = 'training a Gaussian process model and '
[]

[GP_testing]
  type = CSVDiff
  input = GP_squared_exponential_testing.i
  allow_test_objects = true
  csvdiff = 'GP_squared_exponential_testing_out_samp_avg_0002.csv'
  detail = 'evaluating the trained Gaussian process model'
  prereq = store_load/GP_training
[]

[GP_squared_exponential]
  type = CSVDiff
  input = GP_squared_exponential.i
  allow_test_objects = true
  csvdiff = 'GP_squared_exponential_out_samp_avg_0002.csv GP_squared_exponential_out_results_0002.csv'
  detail = 'a squared exponential kernel;'
[]

[GP_exponential]
  type = CSVDiff
  input = GP_exponential.i
  allow_test_objects = true
  csvdiff = 'GP_exponential_out_samp_avg_0002.csv GP_exponential_out_results_0002.csv'
  detail = 'an exponential kernel;'
[]

[GP_Matern_half_int]
  type = CSVDiff
  input = GP_Matern_half_int.i
  allow_test_objects = true
  csvdiff = 'GP_Matern_half_int_out_samp_avg_0002.csv GP_Matern_half_int_out_results_0002.csv'
  detail = 'Matern half integer kernel;'
[]

[GP_squared_exponential_tuned_adam]
  type = CSVDiff
  input = GP_squared_exponential_tuned_adam.i
  allow_test_objects = true
  csvdiff = 'GP_squared_exponential_tuned_adam_out_samp_avg_0002.csv GP_squared_exponential_tuned_adam_out_results_0002.csv GP_squared_exponential_tuned_adam_out_hyperparams_0002.csv'
  detail = 'a squared exponential kernel using Adam;'
[]

[GP_exponential_tuned_adam]
  type = CSVDiff
  input = GP_exponential_tuned_adam.i
  allow_test_objects = true
  csvdiff = 'GP_exponential_tuned_adam_out_samp_avg_0002.csv GP_exponential_tuned_adam_out_results_0002.csv GP_exponential_tuned_adam_out_hyperparams_0002.csv'
  detail = 'an exponential kernel using Adam;'
[]

[GP_Matern_half_int_tuned_adam]
  type = CSVDiff
  input = GP_Matern_half_int_tuned_adam.i
  allow_test_objects = true
  csvdiff = 'GP_Matern_half_int_tuned_adam_samp_avg_0002.csv GP_Matern_half_int_tuned_adam_results_0002.csv GP_Matern_half_int_tuned_adam_hyperparams_0002.csv'
  detail = 'a Matern half integer kernel using Adam;'
[]

[GP_Matern_half_int_tuned_adam_minibatch]
  type = CSVDiff
  input = GP_Matern_half_int_tuned_adam.i
  cli_args = "Trainers/GP_avg_trainer/batch_size=10 Outputs/out/file_base='GP_Matern_half_int_tuned_adam_minibatch'"
  allow_test_objects = true
  csvdiff = 'GP_Matern_half_int_tuned_adam_minibatch_samp_avg_0002.csv GP_Matern_half_int_tuned_adam_minibatch_results_0002.csv GP_Matern_half_int_tuned_adam_minibatch_hyperparams_0002.csv'
  detail = 'a Matern half integer kernel using Adam with mini-batch sampling;'
[]

[optimization_adam_batch_size]
  type = RunException
  input = GP_squared_exponential_tuned_adam.i
  cli_args = "Trainers/GP_avg_trainer/batch_size='100'"
  detail = 'the batch size is greater than the training data set size for Adam optimization. '
  expect_err = "Batch size cannot be greater than the training data set size."
[]

[load]
  type = 'JSONDiff'
  input = mogp_lmc_load.i
  jsondiff = "mogp_lmc_load_out.json"
  detail = "and load the trained surrogate from a file."
  prereq = lmc/tuned
  skip_keys = "reporters train_params"
[]

[GP_training]
  type = CheckFiles
  input = GP_squared_exponential_training.i
  check_files = 'gauss_process_training_GP_avg_trainer.rd/data'
  allow_test_objects = true
  detail = 'training a Gaussian process model and '
[]

[GP_testing]
  type = CSVDiff
  input = GP_squared_exponential_testing.i
  allow_test_objects = true
  csvdiff = 'GP_squared_exponential_testing_out_samp_avg_0002.csv'
  detail = 'evaluating the trained Gaussian process model'
  prereq = store_load/GP_training
[]

[GP_squared_exponential]
  type = CSVDiff
  input = GP_squared_exponential.i
  allow_test_objects = true
  csvdiff = 'GP_squared_exponential_out_samp_avg_0002.csv GP_squared_exponential_out_results_0002.csv'
  detail = 'a squared exponential kernel;'
[]

[GP_exponential]
  type = CSVDiff
  input = GP_exponential.i
  allow_test_objects = true
  csvdiff = 'GP_exponential_out_samp_avg_0002.csv GP_exponential_out_results_0002.csv'
  detail = 'an exponential kernel;'
[]

[GP_Matern_half_int]
  type = CSVDiff
  input = GP_Matern_half_int.i
  allow_test_objects = true
  csvdiff = 'GP_Matern_half_int_out_samp_avg_0002.csv GP_Matern_half_int_out_results_0002.csv'
  detail = 'Matern half integer kernel;'
[]

[GP_squared_exponential_tuned_adam]
  type = CSVDiff
  input = GP_squared_exponential_tuned_adam.i
  allow_test_objects = true
  csvdiff = 'GP_squared_exponential_tuned_adam_out_samp_avg_0002.csv GP_squared_exponential_tuned_adam_out_results_0002.csv GP_squared_exponential_tuned_adam_out_hyperparams_0002.csv'
  detail = 'a squared exponential kernel using Adam;'
[]

[GP_exponential_tuned_adam]
  type = CSVDiff
  input = GP_exponential_tuned_adam.i
  allow_test_objects = true
  csvdiff = 'GP_exponential_tuned_adam_out_samp_avg_0002.csv GP_exponential_tuned_adam_out_results_0002.csv GP_exponential_tuned_adam_out_hyperparams_0002.csv'
  detail = 'an exponential kernel using Adam;'
[]

[GP_Matern_half_int_tuned_adam]
  type = CSVDiff
  input = GP_Matern_half_int_tuned_adam.i
  allow_test_objects = true
  csvdiff = 'GP_Matern_half_int_tuned_adam_samp_avg_0002.csv GP_Matern_half_int_tuned_adam_results_0002.csv GP_Matern_half_int_tuned_adam_hyperparams_0002.csv'
  detail = 'a Matern half integer kernel using Adam;'
[]

[GP_Matern_half_int_tuned_adam_minibatch]
  type = CSVDiff
  input = GP_Matern_half_int_tuned_adam.i
  cli_args = "Trainers/GP_avg_trainer/batch_size=10 Outputs/out/file_base='GP_Matern_half_int_tuned_adam_minibatch'"
  allow_test_objects = true
  csvdiff = 'GP_Matern_half_int_tuned_adam_minibatch_samp_avg_0002.csv GP_Matern_half_int_tuned_adam_minibatch_results_0002.csv GP_Matern_half_int_tuned_adam_minibatch_hyperparams_0002.csv'
  detail = 'a Matern half integer kernel using Adam with mini-batch sampling;'
[]

[optimization_adam_batch_size]
  type = RunException
  input = GP_squared_exponential_tuned_adam.i
  cli_args = "Trainers/GP_avg_trainer/batch_size='100'"
  detail = 'the batch size is greater than the training data set size for Adam optimization. '
  expect_err = "Batch size cannot be greater than the training data set size."
[]

[train_and_evaluate]
  requirement = 'The system shall be able to train and evaluate a libtorch-based neural network in the same input file.'
  type = CSVDiff
  input = train_and_evaluate.i
  allow_test_objects = true
  cli_args = "Trainers/train/read_from_file=false"
  csvdiff = 'train_and_evaluate_out_results_0002.csv'
  libtorch = true
[]

[train]
  requirement = 'The system shall be able to train a libtorch-based neural network.'
  type = CheckFiles
  input = train.i
  check_files = 'train_out_train.rd/data mynet.pt'
  cli_args = "Trainers/train/read_from_file=false"
  allow_test_objects = true
  libtorch = true
[]

[evaluate]
  requirement = 'The system shall be able to evaluate a previously trained, libtorch-based neural network.'
  type = CSVDiff
  input = evaluate.i
  allow_test_objects = true
  csvdiff = 'evaluate_out_results_0002.csv'
  prereq = train
  libtorch = true
[]

[retrain]
  requirement = 'The system shall be able to retrain a pretrained and saved libtorch-based neural network.'
  type = CSVDiff
  input = retrain.i
  allow_test_objects = true
  cli_args = "Trainers/train/read_from_file=true"
  csvdiff = 'retrain_out_results_0002.csv'
  prereq = evaluate
  libtorch = true
[]

[train-with-rel-tol]
  requirement = 'The system shall be able to train a libtorch-based neural network using a relative tolerance instead of fixed epoch number.'
  type = CheckFiles
  input = train.i
  check_files = 'train_out_train.rd/data mynet.pt'
  cli_args = "Trainers/train/read_from_file=false Trainers/train/rel_loss_tol=0.1"
  allow_test_objects = true
  libtorch = true
  prereq = retrain
[]

[train-standardized]
  requirement = 'The system shall be able to train a libtorch-based neural network with standardized input and output parameters.'
  type = CheckFiles
  input = train.i
  check_files = 'standardized_train.rd/data mynet.pt'
  cli_args = "Trainers/train/read_from_file=false Trainers/train/standardize_input=true Trainers/train/standardize_output=true Outputs/file_base=standardized"
  allow_test_objects = true
  libtorch = true
[]

[evaluate-standardized]
  requirement = 'The system shall be able to evaluate a previously trained, libtorch-based neural network with standardized input and output parameters.'
  type = CSVDiff
  input = evaluate.i
  allow_test_objects = true
  csvdiff = 'evaluate_standardized_results_0002.csv'
  cli_args = "Surrogates/surr/filename='standardized_train.rd' Outputs/file_base=evaluate_standardized"
  prereq = train-standardized
  libtorch = true
[]

[train_and_evaluate]
  requirement = 'The system shall be able to train and evaluate a libtorch-based neural network in the same input file.'
  type = CSVDiff
  input = train_and_evaluate.i
  allow_test_objects = true
  cli_args = "Trainers/train/read_from_file=false"
  csvdiff = 'train_and_evaluate_out_results_0002.csv'
  libtorch = true
[]

[train]
  requirement = 'The system shall be able to train a libtorch-based neural network.'
  type = CheckFiles
  input = train.i
  check_files = 'train_out_train.rd/data mynet.pt'
  cli_args = "Trainers/train/read_from_file=false"
  allow_test_objects = true
  libtorch = true
[]

[evaluate]
  requirement = 'The system shall be able to evaluate a previously trained, libtorch-based neural network.'
  type = CSVDiff
  input = evaluate.i
  allow_test_objects = true
  csvdiff = 'evaluate_out_results_0002.csv'
  prereq = train
  libtorch = true
[]

[retrain]
  requirement = 'The system shall be able to retrain a pretrained and saved libtorch-based neural network.'
  type = CSVDiff
  input = retrain.i
  allow_test_objects = true
  cli_args = "Trainers/train/read_from_file=true"
  csvdiff = 'retrain_out_results_0002.csv'
  prereq = evaluate
  libtorch = true
[]

[train-with-rel-tol]
  requirement = 'The system shall be able to train a libtorch-based neural network using a relative tolerance instead of fixed epoch number.'
  type = CheckFiles
  input = train.i
  check_files = 'train_out_train.rd/data mynet.pt'
  cli_args = "Trainers/train/read_from_file=false Trainers/train/rel_loss_tol=0.1"
  allow_test_objects = true
  libtorch = true
  prereq = retrain
[]

[train-standardized]
  requirement = 'The system shall be able to train a libtorch-based neural network with standardized input and output parameters.'
  type = CheckFiles
  input = train.i
  check_files = 'standardized_train.rd/data mynet.pt'
  cli_args = "Trainers/train/read_from_file=false Trainers/train/standardize_input=true Trainers/train/standardize_output=true Outputs/file_base=standardized"
  allow_test_objects = true
  libtorch = true
[]

[evaluate-standardized]
  requirement = 'The system shall be able to evaluate a previously trained, libtorch-based neural network with standardized input and output parameters.'
  type = CSVDiff
  input = evaluate.i
  allow_test_objects = true
  csvdiff = 'evaluate_standardized_results_0002.csv'
  cli_args = "Surrogates/surr/filename='standardized_train.rd' Outputs/file_base=evaluate_standardized"
  prereq = train-standardized
  libtorch = true
[]

[load]
  type = 'JSONDiff'
  input = mogp_lmc_load.i
  jsondiff = "mogp_lmc_load_out.json"
  detail = "and load the trained surrogate from a file."
  prereq = lmc/tuned
  skip_keys = "reporters train_params"
[]

[load]
  type = 'JSONDiff'
  input = mogp_lmc_load.i
  jsondiff = "mogp_lmc_load_out.json"
  detail = "and load the trained surrogate from a file."
  prereq = lmc/tuned
  skip_keys = "reporters train_params"
[]

[load]
  type = CSVDiff
  input = evaluate.i
  allow_test_objects = true
  csvdiff = 'evaluate_out_results_0002.csv'
  prereq = nearest_point/train # Creates data files that this test uses
  detail = 'evaluating, '
[]

[predictors]
  type = CSVDiff
  input = predictor_response.i
  allow_test_objects = true
  cli_args = 'Outputs/file_base=evaluate_out'
  csvdiff = 'evaluate_out_results_0002.csv'
  detail = 'using explictly specified predictors.'
[]

[load]
  type = CSVDiff
  input = evaluate.i
  allow_test_objects = true
  csvdiff = 'evaluate_out_results_0002.csv'
  prereq = nearest_point/train # Creates data files that this test uses
  detail = 'evaluating, '
[]

[predictors]
  type = CSVDiff
  input = predictor_response.i
  allow_test_objects = true
  cli_args = 'Outputs/file_base=evaluate_out'
  csvdiff = 'evaluate_out_results_0002.csv'
  detail = 'using explictly specified predictors.'
[]

[train]
  type = CheckFiles
  input = trainer.i
  check_files = 'trainer_out_pod_rb.rd/data'
  allow_test_objects = true
  detail = 'training using known 4D data '
  max_parallel = 5 # We need fewer processors than samples
[]

[load]
  type = CSVDiff
  input = surr.i
  allow_test_objects = true
  csvdiff = 'surr_out_res_0001.csv'
  prereq = pod_boundary/train # Creates data files that this test uses
  detail = 'and then evaluating new samples separately for new data.'
[]

[var_names]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = "Trainers/pod_rb/var_names='doesnt_exist'"
  detail = 'the variable names cannot be found on sub-applications, '
  expect_err = "Variable 'doesnt_exist' not found on sub-application [0-9]+!"
  max_parallel = 3
[]

[en_limits]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = "Trainers/pod_rb/error_res='1e-2 1e-3'"
  detail = 'the number of energy limits and variable names do not match, '
  expect_err = "The number of elements is not equal to the number of elements in 'var_names'!"
  max_parallel = 3
[]

[tag_types]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = 'Trainers/pod_rb/tag_types="op"'
  detail = 'the number of tag names and tag types do not match, '
  expect_err = "The number of elements is not equal to the number of elements in 'tag_names'!"
  max_parallel = 3
[]

[dir_tag_names]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = 'Trainers/pod_rb/tag_types="doesnt_exist doesnt_exist doesnt_exist"'
  detail = "the Dirichlet tag types do not exist, "
  expect_err = "Tag type 'doesnt_exist' is not valid, available names are: op, op_dir, src, src_dir"
  max_parallel = 3
[]

[wrong_res_transfer]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = 'Trainers/pod_rb/execute_on="final"'
  detail = 'and the residual generation is called before having the basis vectors.'
  expect_err = "There are no basis vectors available for residual generation. This indicates that the bases have not been created yet. The most common cause of this error is the wrong setting of the 'execute_on' flags in the PODFullSolveMultiApp and/or PODReducedBasisTrainer."
  match_literal = true
  max_parallel = 3
[]

[train]
  type = CheckFiles
  input = trainer.i
  check_files = 'trainer_out_pod_rb.rd/data'
  allow_test_objects = true
  detail = 'training using known 3D data, '
  max_parallel = 3 # We need fewer processors than samples
[]

[save_ev]
  type = CSVDiff
  input = trainer.i
  csvdiff = 'eigenvalues_u.csv'
  cli_args = "Trainers/pod_rb/filenames='eigenvalues_u.csv'"
  allow_test_objects = true
  detail = 'saving the eigenvalues, '
  max_parallel = 3 # We need fewer processors than samples
[]

[load]
  type = CSVDiff
  input = surr.i
  allow_test_objects = true
  csvdiff = 'surr_out_res_0001.csv'
  prereq = pod_internal/train # Creates data files that this test uses
  detail = 'then evaluating new samples separately for new data, '
[]

[train_and_load]
  type = CSVDiff
  input = trainer_and_surr.i
  allow_test_objects = true
  csvdiff = 'trainer_and_surr_out_res_0002.csv'
  detail = 'and doing both together in one input file.'
  max_parallel = 3 # We need fewer processors than samples
[]

[train]
  type = CheckFiles
  input = trainer.i
  check_files = 'trainer_out_pod_rb.rd/data'
  allow_test_objects = true
  detail = 'training using known 4D data '
  max_parallel = 5 # We need fewer processors than samples
[]

[load]
  type = CSVDiff
  input = surr.i
  allow_test_objects = true
  csvdiff = 'surr_out_res_0001.csv'
  prereq = pod_boundary/train # Creates data files that this test uses
  detail = 'and then evaluating new samples separately for new data.'
[]

[var_name]
  type = RunException
  input = trainer_and_surr.i
  allow_test_objects = true
  cli_args = "Surrogates/rbpod/change_rank='doesnt_exist'"
  detail = "the number of inputs in 'change_rank' and 'new_ranks' is not the same."
  expect_err = "The size of 'new_ranks' is not equal to the size of 'change_rank' 0 != 1"
[]

[train]
  type = CheckFiles
  input = trainer.i
  check_files = 'trainer_out_pod_rb.rd/data'
  allow_test_objects = true
  detail = 'training using known 3D data, '
  max_parallel = 3 # We need fewer processors than samples
[]

[save_ev]
  type = CSVDiff
  input = trainer.i
  csvdiff = 'eigenvalues_u.csv'
  cli_args = "Trainers/pod_rb/filenames='eigenvalues_u.csv'"
  allow_test_objects = true
  detail = 'saving the eigenvalues, '
  max_parallel = 3 # We need fewer processors than samples
[]

[load]
  type = CSVDiff
  input = surr.i
  allow_test_objects = true
  csvdiff = 'surr_out_res_0001.csv'
  prereq = pod_internal/train # Creates data files that this test uses
  detail = 'then evaluating new samples separately for new data, '
[]

[train_and_load]
  type = CSVDiff
  input = trainer_and_surr.i
  allow_test_objects = true
  csvdiff = 'trainer_and_surr_out_res_0002.csv'
  detail = 'and doing both together in one input file.'
  max_parallel = 3 # We need fewer processors than samples
[]

[train]
  type = CheckFiles
  input = trainer.i
  check_files = 'trainer_out_pod_rb.rd/data'
  allow_test_objects = true
  detail = 'training using known 4D data '
  max_parallel = 5 # We need fewer processors than samples
[]

[load]
  type = CSVDiff
  input = surr.i
  allow_test_objects = true
  csvdiff = 'surr_out_res_0001.csv'
  prereq = pod_boundary/train # Creates data files that this test uses
  detail = 'and then evaluating new samples separately for new data.'
[]

[trainer_name]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = 'MultiApps/sub/trainer_name="doesnt_exist"'
  detail = 'the trainer object cannot be found.'
  expect_err = "Unable to find user object with name 'doesnt_exist'"
  max_parallel = 3
[]

[max_procs_per_app]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = 'MultiApps/sub/max_procs_per_app=2 Samplers/sample/max_procs_per_row=2'
  detail = 'the trainer is run with more than one processors per subapp.'
  expect_err = "does not support more than one processors per subapp"
[]

[more_procs_than_samples]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  detail = 'the number of samples is lower than the number of processors.'
  expect_err = "PODFullSolveMultiApp needs to be run with fewer processors \(detected (\d+)\) than samples \(detected 3\)"
  min_parallel = 4
[]

[train]
  type = CheckFiles
  input = trainer.i
  check_files = 'trainer_out_pod_rb.rd/data'
  allow_test_objects = true
  detail = 'training using known 3D data, '
  max_parallel = 3 # We need fewer processors than samples
[]

[save_ev]
  type = CSVDiff
  input = trainer.i
  csvdiff = 'eigenvalues_u.csv'
  cli_args = "Trainers/pod_rb/filenames='eigenvalues_u.csv'"
  allow_test_objects = true
  detail = 'saving the eigenvalues, '
  max_parallel = 3 # We need fewer processors than samples
[]

[load]
  type = CSVDiff
  input = surr.i
  allow_test_objects = true
  csvdiff = 'surr_out_res_0001.csv'
  prereq = pod_internal/train # Creates data files that this test uses
  detail = 'then evaluating new samples separately for new data, '
[]

[train_and_load]
  type = CSVDiff
  input = trainer_and_surr.i
  allow_test_objects = true
  csvdiff = 'trainer_and_surr_out_res_0002.csv'
  detail = 'and doing both together in one input file.'
  max_parallel = 3 # We need fewer processors than samples
[]

[train]
  type = CheckFiles
  input = trainer.i
  check_files = 'trainer_out_pod_rb.rd/data'
  allow_test_objects = true
  detail = 'training using known 4D data '
  max_parallel = 5 # We need fewer processors than samples
[]

[load]
  type = CSVDiff
  input = surr.i
  allow_test_objects = true
  csvdiff = 'surr_out_res_0001.csv'
  prereq = pod_boundary/train # Creates data files that this test uses
  detail = 'and then evaluating new samples separately for new data.'
[]

[trainer_name]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = 'Transfers/snapshot/trainer_name="doesnt_exist"'
  detail = 'the trainer object cannot be found.'
  expect_err = "Unable to find user object with name 'doesnt_exist'"
  max_parallel = 3
[]

[var_names]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = "Trainers/pod_rb/var_names='doesnt_exist'"
  detail = 'the variable names cannot be found on sub-applications, '
  expect_err = "Variable 'doesnt_exist' not found on sub-application [0-9]+!"
  max_parallel = 3
[]

[en_limits]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = "Trainers/pod_rb/error_res='1e-2 1e-3'"
  detail = 'the number of energy limits and variable names do not match, '
  expect_err = "The number of elements is not equal to the number of elements in 'var_names'!"
  max_parallel = 3
[]

[tag_types]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = 'Trainers/pod_rb/tag_types="op"'
  detail = 'the number of tag names and tag types do not match, '
  expect_err = "The number of elements is not equal to the number of elements in 'tag_names'!"
  max_parallel = 3
[]

[dir_tag_names]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = 'Trainers/pod_rb/tag_types="doesnt_exist doesnt_exist doesnt_exist"'
  detail = "the Dirichlet tag types do not exist, "
  expect_err = "Tag type 'doesnt_exist' is not valid, available names are: op, op_dir, src, src_dir"
  max_parallel = 3
[]

[wrong_res_transfer]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = 'Trainers/pod_rb/execute_on="final"'
  detail = 'and the residual generation is called before having the basis vectors.'
  expect_err = "There are no basis vectors available for residual generation. This indicates that the bases have not been created yet. The most common cause of this error is the wrong setting of the 'execute_on' flags in the PODFullSolveMultiApp and/or PODReducedBasisTrainer."
  match_literal = true
  max_parallel = 3
[]

[train]
  type = CheckFiles
  input = trainer.i
  check_files = 'trainer_out_pod_rb.rd/data'
  allow_test_objects = true
  detail = 'training using known 3D data, '
  max_parallel = 3 # We need fewer processors than samples
[]

[save_ev]
  type = CSVDiff
  input = trainer.i
  csvdiff = 'eigenvalues_u.csv'
  cli_args = "Trainers/pod_rb/filenames='eigenvalues_u.csv'"
  allow_test_objects = true
  detail = 'saving the eigenvalues, '
  max_parallel = 3 # We need fewer processors than samples
[]

[load]
  type = CSVDiff
  input = surr.i
  allow_test_objects = true
  csvdiff = 'surr_out_res_0001.csv'
  prereq = pod_internal/train # Creates data files that this test uses
  detail = 'then evaluating new samples separately for new data, '
[]

[train_and_load]
  type = CSVDiff
  input = trainer_and_surr.i
  allow_test_objects = true
  csvdiff = 'trainer_and_surr_out_res_0002.csv'
  detail = 'and doing both together in one input file.'
  max_parallel = 3 # We need fewer processors than samples
[]

[train]
  type = CheckFiles
  input = trainer.i
  check_files = 'trainer_out_pod_rb.rd/data'
  allow_test_objects = true
  detail = 'training using known 4D data '
  max_parallel = 5 # We need fewer processors than samples
[]

[load]
  type = CSVDiff
  input = surr.i
  allow_test_objects = true
  csvdiff = 'surr_out_res_0001.csv'
  prereq = pod_boundary/train # Creates data files that this test uses
  detail = 'and then evaluating new samples separately for new data.'
[]

[var_names]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = "Trainers/pod_rb/var_names='doesnt_exist'"
  detail = 'the variable names cannot be found on sub-applications, '
  expect_err = "Variable 'doesnt_exist' not found on sub-application [0-9]+!"
  max_parallel = 3
[]

[en_limits]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = "Trainers/pod_rb/error_res='1e-2 1e-3'"
  detail = 'the number of energy limits and variable names do not match, '
  expect_err = "The number of elements is not equal to the number of elements in 'var_names'!"
  max_parallel = 3
[]

[tag_types]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = 'Trainers/pod_rb/tag_types="op"'
  detail = 'the number of tag names and tag types do not match, '
  expect_err = "The number of elements is not equal to the number of elements in 'tag_names'!"
  max_parallel = 3
[]

[dir_tag_names]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = 'Trainers/pod_rb/tag_types="doesnt_exist doesnt_exist doesnt_exist"'
  detail = "the Dirichlet tag types do not exist, "
  expect_err = "Tag type 'doesnt_exist' is not valid, available names are: op, op_dir, src, src_dir"
  max_parallel = 3
[]

[wrong_res_transfer]
  type = RunException
  input = trainer.i
  allow_test_objects = true
  cli_args = 'Trainers/pod_rb/execute_on="final"'
  detail = 'and the residual generation is called before having the basis vectors.'
  expect_err = "There are no basis vectors available for residual generation. This indicates that the bases have not been created yet. The most common cause of this error is the wrong setting of the 'execute_on' flags in the PODFullSolveMultiApp and/or PODReducedBasisTrainer."
  match_literal = true
  max_parallel = 3
[]

[train]
  type = CheckFiles
  input = trainer.i
  check_files = 'trainer_out_pod_rb.rd/data'
  allow_test_objects = true
  detail = 'training using known 3D data, '
  max_parallel = 3 # We need fewer processors than samples
[]

[save_ev]
  type = CSVDiff
  input = trainer.i
  csvdiff = 'eigenvalues_u.csv'
  cli_args = "Trainers/pod_rb/filenames='eigenvalues_u.csv'"
  allow_test_objects = true
  detail = 'saving the eigenvalues, '
  max_parallel = 3 # We need fewer processors than samples
[]

[load]
  type = CSVDiff
  input = surr.i
  allow_test_objects = true
  csvdiff = 'surr_out_res_0001.csv'
  prereq = pod_internal/train # Creates data files that this test uses
  detail = 'then evaluating new samples separately for new data, '
[]

[train_and_load]
  type = CSVDiff
  input = trainer_and_surr.i
  allow_test_objects = true
  csvdiff = 'trainer_and_surr_out_res_0002.csv'
  detail = 'and doing both together in one input file.'
  max_parallel = 3 # We need fewer processors than samples
[]

[monte_carlo_integration]
  type = CSVDiff
  input = main_2d_mc.i
  allow_test_objects = true
  csvdiff = 'main_2d_mc_out_pc_samp_0002.csv'
  detail = 'MonteCarlo sampler with Uniform distribution, '
[]

[monte_carlo_ridge]
  type = JSONDiff
  input = ols_test.i
  cli_args = 'Trainers/train_ols/penalty=1.0 Trainers/train_pet_ols/penalty=1.0 Outputs/file_base=ridge_test_out'
  jsondiff = ridge_test_out.json
  detail = 'MonteCarlo sampler with Uniform distribution using Ridge regression, '
[]

[gauss_legendre_integration]
  type = CSVDiff
  input = main_2d_quad.i
  allow_test_objects = true
  csvdiff = 'main_2d_quad_out_pc_samp_0002.csv'
  detail = 'Quadrature sampler with Uniform distribution, and '
[]

[gauss_hermite]
  type = CSVDiff
  input = main_2dnorm_quad.i
  allow_test_objects = true
  csvdiff = 'main_2dnorm_quad_out_pc_samp_0002.csv'
  detail = 'Quadrature sampler with Normal distribution.'
[]

[legendre_moment_calculation]
  type = JSONDiff
  input = main_2d_quad_moment.i
  allow_test_objects = true
  jsondiff = 'main_2d_quad_moment_out.json'
  detail = 'statistical moments with Legendre polynomials, '
[]

[hermite_moment_calculation]
  type = JSONDiff
  input = main_2dnorm_quad_moment.i
  allow_test_objects = true
  jsondiff = 'main_2dnorm_quad_moment_out.json'
  detail = 'statistical moments with Hermite polynomials, '
[]

[legendre_local_sensitivity]
  type = JSONDiff
  input = main_2d_quad_locs.i
  allow_test_objects = true
  jsondiff = 'main_2d_quad_locs_out.json'
  detail = 'sampler and user defined local sensitivities with Legendre polynomials, '
[]

[hermite_local_sensitivity]
  type = JSONDiff
  input = main_2dnorm_quad_locs.i
  allow_test_objects = true
  jsondiff = 'main_2dnorm_quad_locs_out.json'
  detail = 'sampler and user defined local sensitivities with Hermite polynomials, and '
[]

[smolyak_sobol]
  type = JSONDiff
  input = sobol.i
  allow_test_objects = true
  cli_args = 'Samplers/sample/sparse_grid=smolyak Outputs/file_base=smolyak'
  jsondiff = 'smolyak.json'
  detail = 'Smolyak and '
[]

[clenshaw_curtis_sobol]
  type = JSONDiff
  input = sobol.i
  allow_test_objects = true
  cli_args = 'Samplers/sample/sparse_grid=clenshaw-curtis Outputs/file_base=clenshaw_curtis'
  jsondiff = 'clenshaw_curtis.json'
  detail = 'Clenshaw-Curtis methods.'
[]

[wrong_sampler]
  type = RunException
  input = error.i
  allow_test_objects = true
  expect_err = "Reporter value values/g_values of size [0-9]{3,4} does not match sampler size \([0-9]{2,3}\)."
  detail = "the number of samples does not match the number of results."
[]

[load]
  type = CSVDiff
  input = evaluate.i
  allow_test_objects = true
  csvdiff = 'evaluate_out_results_0002.csv'
  prereq = poly_reg/train # Creates data files that this test uses
  detail = 'and then evaluating new samples separately for the same data'
[]

[train_and_load]
  type = CSVDiff
  input = train_and_evaluate.i
  allow_test_objects = true
  csvdiff = 'train_and_evaluate_out_results_0002.csv'
  detail = 'and then doing both on another 1D case.'
[]

[vector]
  type = JSONDiff
  input = poly_reg_vec.i
  jsondiff = 'poly_reg_vec_out.json'
  requirement = 'The system shall be able to create polynomial regression surrogate with vector-type response.'
[]

[load]
  type = CSVDiff
  input = evaluate.i
  allow_test_objects = true
  csvdiff = 'evaluate_out_results_0002.csv'
  prereq = poly_reg/train # Creates data files that this test uses
  detail = 'and then evaluating new samples separately for the same data'
[]

[train_and_load]
  type = CSVDiff
  input = train_and_evaluate.i
  allow_test_objects = true
  csvdiff = 'train_and_evaluate_out_results_0002.csv'
  detail = 'and then doing both on another 1D case.'
[]

[vector]
  type = JSONDiff
  input = poly_reg_vec.i
  jsondiff = 'poly_reg_vec_out.json'
  requirement = 'The system shall be able to create polynomial regression surrogate with vector-type response.'
[]

[torch-parameters]
  type = JSONDiff
  input = libtorch_drl_control_trainer.i
  jsondiff = 'train_out.json train_out_runner0_json_out.json'
  skip_keys = 'reporters time time_step'
  requirement = "The system shall be able to transfer an artificial neural network between a trainer object and a control object residing on different apps."
  libtorch = true
[]

[wrong_multi_app]
  type = RunException
  input = wrong_multi_app.i
  expect_err = "The 'multi_app' parameter must provide either a 'SamplerTransientMultiApp' or 'SamplerFullSolveMultiApp' object."
  requirement = "The system shall produce an error if neither a 'SamplerTransientMultiApp' nor `SamplerFullSolveMultiApp` is provided in SamplerPostprocessorTransfer."
[]

[require_stochastic_results]
  type = RunException
  input = require_stochastic_results.i
  expect_err = "The object prescribed by the 'to_vector_postprocessor' parameter in data must be a 'StochasticResults' object"
  requirement = "The system shall produce an error if the 'result' object in 'SamplerPostprocessorTransfer' is not a 'StochasticResults object'."
[]

[multiple]
  type = CSVDiff
  input = parent.i
  csvdiff = parent_out_storage_0002.csv

  requirement = "The system shall support the collection of stochastic data from multiple sub-applications."
[]

[replicated]
  type = CSVDiff
  input = parent.i
  cli_args = 'VectorPostprocessors/storage/parallel_type=REPLICATED Outputs/file_base=replicated_out'
  csvdiff = replicated_out_storage_0002.csv

  detail = "replicated on all processors and"
[]

[distributed]
  type = CSVDiff
  input = parent.i
  min_parallel = 3
  max_parallel = 3
  cli_args = 'VectorPostprocessors/storage/parallel_type=DISTRIBUTED Outputs/file_base=distributed_out'
  csvdiff = 'distributed_out_storage_0002.csv.0 distributed_out_storage_0002.csv.1 distributed_out_storage_0002.csv.2'

  detail = "distributed across many."
[]

[custom]
  type = CSVDiff
  input = parent.i
  cli_args = 'VectorPostprocessors/storage/parallel_type=REPLICATED Outputs/file_base=prefix_out Transfers/data/prefix=prefix'
  csvdiff = prefix_out_storage_0002.csv

  detail = "with custom prefix and"
[]

[none]
  type = CSVDiff
  input = parent.i
  cli_args = 'VectorPostprocessors/storage/parallel_type=REPLICATED Outputs/file_base=no_prefix_out Transfers/data/prefix='
  csvdiff = no_prefix_out_storage_0002.csv

  detail = "without a prefix."
[]

[time]
  type = CSVDiff
  input = parent.i
  cli_args = VectorPostprocessors/storage/contains_complete_history=false
  csvdiff = 'parent_out_storage_0000.csv parent_out_storage_0001.csv parent_out_storage_0002.csv'

  detail = "or contain a single file per timestep."
[]

[distributed]
  type = JSONDiff
  input = main.i
  cli_args = 'Reporters/storage/parallel_type=DISTRIBUTED Outputs/file_base=main_parallel'
  jsondiff = 'main_parallel.json main_parallel.json.1 main_parallel.json.2 main_parallel.json.3'
  min_parallel = 4
  max_parallel = 4

  detail = 'with distributed output, '
[]

[invalid_type]
  type = RunException
  input = main.i
  cli_args = "Transfers/data/from_reporter='mesh/sidesets'"
  expect_err = 'Reporter value mesh/sidesets is of unsupported type'

  detail = 'and error if transferring unsupported type.'
[]

[batch_restore]
  detail = "batch-restore mode,"
  type = JSONDiff
  input = sst_main.i
  jsondiff = 'serialization_batch_restore.json serialization_batch_restore.json.1 serialization_batch_restore.json.2 serialization_batch_restore.json.3'
  cli_args = "Outputs/file_base=serialization_batch_restore Transfers/solution_transfer/serialize_on_root=true MultiApps/worker/mode=batch-restore"
  min_parallel = 4
  max_parallel = 4
[]

[batch_reset]
  detail = "batch-reset mode, and"
  type = JSONDiff
  input = sst_main.i
  jsondiff = 'serialization_batch_reset.json serialization_batch_reset.json.1 serialization_batch_reset.json.2 serialization_batch_reset.json.3'
  cli_args = "Outputs/file_base=serialization_batch_reset Transfers/solution_transfer/serialize_on_root=true MultiApps/worker/mode=batch-reset"
  min_parallel = 4
  max_parallel = 4
[]

[normal]
  detail = "normal mode."
  type = JSONDiff
  input = sst_main.i
  jsondiff = 'serialization_normal.json serialization_normal.json.1 serialization_normal.json.2 serialization_normal.json.3'
  cli_args = "Outputs/file_base=serialization_normal Transfers/solution_transfer/serialize_on_root=true MultiApps/worker/mode=normal"
  min_parallel = 4
  max_parallel = 4
[]

[batch_restore]
  detail = "batch-restore mode,"
  type = JSONDiff
  input = sst_main.i
  jsondiff = 'no_serialization_batch_restore.json no_serialization_batch_restore.json.1 no_serialization_batch_restore.json.2 no_serialization_batch_restore.json.3'
  cli_args = "Outputs/file_base=no_serialization_batch_restore Transfers/solution_transfer/serialize_on_root=false MultiApps/worker/mode=batch-restore Transfers/solution_transfer/variables='u' Reporters/parallel_storage/variables='u'"
  min_parallel = 4
  max_parallel = 4
[]

[batch_reset]
  detail = "batch-reset mode, and"
  type = JSONDiff
  input = sst_main.i
  jsondiff = 'no_serialization_batch_reset.json no_serialization_batch_reset.json.1 no_serialization_batch_reset.json.2 no_serialization_batch_reset.json.3'
  cli_args = "Outputs/file_base=no_serialization_batch_reset Transfers/solution_transfer/serialize_on_root=false MultiApps/worker/mode=batch-reset Transfers/solution_transfer/variables='u' Reporters/parallel_storage/variables='u'"
  min_parallel = 4
  max_parallel = 4
[]

[normal]
  detail = "normal mode."
  type = JSONDiff
  input = sst_main.i
  jsondiff = 'no_serialization_normal.json no_serialization_normal.json.1 no_serialization_normal.json.2 no_serialization_normal.json.3'
  cli_args = "Outputs/file_base=no_serialization_normal Transfers/solution_transfer/serialize_on_root=false MultiApps/worker/mode=normal Transfers/solution_transfer/variables='u' Reporters/parallel_storage/variables='u'"
  min_parallel = 4
  max_parallel = 4
[]

[create_surrogate]
  requirement = 'The system shall be able to create a surrogate model for the solution coordinates in the latent space.'
  type = CheckFiles
  input = create_mapping_main.i
  check_files = 'create_mapping_main_rom_polyreg_v.rd/data create_mapping_main_rom_polyreg_v_aux.rd/data create_mapping_main_mapping_pod_mapping.rd/data'
  min_parallel = 2
  max_parallel = 2
  petsc_version = ">=3.14"
[]

[with_surrogate]
  detail = 'coordinates determined by a surrogate model.'
  type = Exodiff
  input = inverse_map.i
  cli_args = "Outputs/file_base='with_surrogate'"
  exodiff = 'with_surrogate.e'
  min_parallel = 2
  max_parallel = 2
  prereq = create_surrogate
  petsc_version = ">=3.14"
[]

[with_manual_input]
  detail = 'manually specified coordinates.'
  type = Exodiff
  input = inverse_map.i
  cli_args = "Outputs/file_base='with_manual' UserObjects/im/surrogate=''"
  exodiff = 'with_manual.e'
  min_parallel = 2
  max_parallel = 2
  prereq = create_surrogate
  petsc_version = ">=3.14"
[]

[with_polyreg_csv]
  # Note: the reconstructed solution with 2 POD modes and a linear regression should be exact.
  detail = 'polynomial regression surrogates for the expansion coefficients.'
  type = CSVDiff
  input = inverse_map.i
  cli_args = "Outputs/file_base='with_surrogate'"
  csvdiff = 'with_surrogate.csv'
  min_parallel = 2
  max_parallel = 2
  prereq = create_surrogate
  petsc_version = ">=3.14"
[]

[svd_1_proc_per_app]
  type = JSONDiff
  requirement = 'The system should be able to build a variable mapping by computing the SVD of a snapshot matrix in serial.'
  input = pod_mapping_main.i
  jsondiff = 'serial.json'
  cli_args = "Outputs/file_base=serial"
  skip_keys = "left_singular_vectors right_singular_vectors" # We do this because the negative of the eigenvectors are still eigenvectors and depending on the system it finds alternating signs
  max_parallel = 1
  petsc_version = ">=3.14"
[]

[svd_2_proc_per_app]
  type = JSONDiff
  requirement = 'The system should be able to build a variable mapping by computing the SVD of a snapshot matrix in parallel.'
  input = pod_mapping_main.i
  jsondiff = 'two_procs_per_app.json'
  skip_keys = "left_singular_vectors right_singular_vectors" # We do this because the negative of the eigenvectors are still eigenvectors and depending on the system it finds alternating signs
  cli_args = "Outputs/file_base=two_procs_per_app Transfers/solution_transfer/serialize_on_root=false"
  min_parallel = 2
  max_parallel = 2
  petsc_version = ">=3.14"
[]

[svd_2_proc_per_app_root]
  type = JSONDiff
  requirement = 'The system should be able to build a variable mapping by computing the SVD of a snapshot matrix in parallel with having data on only root processors.'
  input = pod_mapping_main.i
  jsondiff = 'two_procs_per_app_root.json'
  cli_args = "Outputs/file_base=two_procs_per_app_root Transfers/solution_transfer/serialize_on_root=true"
  skip_keys = "left_singular_vectors right_singular_vectors" # We do this because the negative of the eigenvectors are still eigenvectors and depending on the system it finds alternating signs
  min_parallel = 2
  max_parallel = 2
  petsc_version = ">=3.14"
[]

[get_global_samples]
  type = CSVDiff
  input = sampler_data.i
  cli_args = 'VectorPostprocessors/data/sampler_method=get_global_samples'
  csvdiff = sampler_data_out_data_0001.csv
  detail = 'get global matrix;'
[]

[get_local_samples]
  type = CSVDiff
  input = sampler_data.i
  cli_args = 'VectorPostprocessors/data/sampler_method=get_local_samples'
  csvdiff = sampler_data_out_data_0001.csv
  detail = 'get local matrix;'
[]

[get_next_local_row]
  type = CSVDiff
  input = sampler_data.i
  cli_args = 'VectorPostprocessors/data/sampler_method=get_next_local_row'
  csvdiff = sampler_data_out_data_0001.csv
  detail = 'get next local row;'
[]

[get_local_samples]
  type = CSVDiff
  input = sampler_data.i
  cli_args = 'VectorPostprocessors/data/sampler_method=get_local_samples  VectorPostprocessors/data/parallel_type=distributed'
  csvdiff = 'sampler_data_out_data_0001.csv.0 sampler_data_out_data_0001.csv.1 sampler_data_out_data_0001.csv.2'
  min_parallel = 3
  max_parallel = 3
  detail = 'get local matrix;'
[]

[get_next_local_row]
  type = CSVDiff
  input = sampler_data.i
  cli_args = 'VectorPostprocessors/data/sampler_method=get_next_local_row  VectorPostprocessors/data/parallel_type=distributed'
  csvdiff = 'sampler_data_out_data_0001.csv.0 sampler_data_out_data_0001.csv.1 sampler_data_out_data_0001.csv.2'
  min_parallel = 3
  max_parallel = 3
  detail = 'get next local row;'
[]

[multi]
  type = CSVDiff
  input = sampler_data.i
  cli_args = "Samplers/sample/distributions='dist dist dist' Outputs/file_base=multi_col"
  csvdiff = multi_col_data_0001.csv
  detail = 'multiple columns;'
[]

[lots]
  type = CSVDiff
  input = sampler_data.i
  cli_args = "Samplers/sample/distributions='dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist dist' Outputs/file_base=lots_col"
  csvdiff = lots_col_data_0001.csv
  detail = 'large number of columns;'
[]

[sobol]
  type = CSVDiff
  input = sobol.i
  csvdiff = sobol_out_sobol_0002.csv
  allow_test_objects = true

  requirement = "The system shall support the ability to compute first, second, and total-effect Sobol sensitivity indices."
[]

[sobol_bootstrap]
  type = CSVDiff
  input = sobol_bootstrap.i
  csvdiff = sobol_bootstrap_out_sobol_0002.csv
  allow_test_objects = true

  requirement = "The system shall support the ability to compute confidence intervals on Sobol sensitivity indices."
[]