ParameterStudyAction

This class defines the behavior of the ParameterStudy input block, please refer to ParameterStudy for more information.

Input Parameters

  • inputThe input file containing the physics for the parameter study.

    C++ Type:FileName

    Unit:(no unit assumed)

    Controllable:No

    Description:The input file containing the physics for the parameter study.

  • parametersList of parameters being perturbed for the study.

    C++ Type:std::vector<std::string>

    Unit:(no unit assumed)

    Controllable:No

    Description:List of parameters being perturbed for the study.

  • sampling_typeThe type of sampling to use for the parameter study.

    C++ Type:MooseEnum

    Unit:(no unit assumed)

    Options:monte-carlo, lhs, cartesian-product, csv, input-matrix

    Controllable:No

    Description:The type of sampling to use for the parameter study.

Required Parameters

  • active__all__ If specified only the blocks named will be visited and made active

    Default:__all__

    C++ Type:std::vector<std::string>

    Unit:(no unit assumed)

    Controllable:No

    Description:If specified only the blocks named will be visited and made active

  • ci_levels0.01 0.05 0.1 0.9 0.95 0.99 A vector of confidence levels to consider for statistics confidence intervals, values must be in (0, 1).

    Default:0.01 0.05 0.1 0.9 0.95 0.99

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:A vector of confidence levels to consider for statistics confidence intervals, values must be in (0, 1).

  • ci_replicates1000The number of replicates to use when computing confidence level intervals for statistics.

    Default:1000

    C++ Type:unsigned int

    Unit:(no unit assumed)

    Controllable:No

    Description:The number of replicates to use when computing confidence level intervals for statistics.

  • compute_statisticsTrueWhether or not to compute statistics on the 'quantities_of_interest'. The default is to compute mean and standard deviation with 0.01, 0.05, 0.1, 0.9, 0.95, and 0.99 confidence intervals.

    Default:True

    C++ Type:bool

    Unit:(no unit assumed)

    Controllable:No

    Description:Whether or not to compute statistics on the 'quantities_of_interest'. The default is to compute mean and standard deviation with 0.01, 0.05, 0.1, 0.9, 0.95, and 0.99 confidence intervals.

  • csv_column_indicesColumn indices in the CSV file to be sampled from for 'csv' sampling. Number of indices here will be the same as the number of columns per matrix.

    C++ Type:std::vector<unsigned long>

    Unit:(no unit assumed)

    Controllable:No

    Description:Column indices in the CSV file to be sampled from for 'csv' sampling. Number of indices here will be the same as the number of columns per matrix.

  • csv_column_namesColumn names in the CSV file to be sampled from for 'csv' sampling. Number of columns names here will be the same as the number of columns per matrix.

    C++ Type:std::vector<std::string>

    Unit:(no unit assumed)

    Controllable:No

    Description:Column names in the CSV file to be sampled from for 'csv' sampling. Number of columns names here will be the same as the number of columns per matrix.

  • csv_samples_fileName of the CSV file that contains the sample matrix for 'csv' sampling.

    C++ Type:FileName

    Unit:(no unit assumed)

    Controllable:No

    Description:Name of the CSV file that contains the sample matrix for 'csv' sampling.

  • distributionsThe types of distribution to use for 'monte-carlo' and 'lhs' sampling. The number of entries defines the number of columns in the matrix.

    C++ Type:MultiMooseEnum

    Unit:(no unit assumed)

    Options:normal, uniform, weibull, lognormal, tnormal

    Controllable:No

    Description:The types of distribution to use for 'monte-carlo' and 'lhs' sampling. The number of entries defines the number of columns in the matrix.

  • ignore_solve_not_convergeFalseTrue to continue main app even if a sub app's solve does not converge.

    Default:False

    C++ Type:bool

    Unit:(no unit assumed)

    Controllable:No

    Description:True to continue main app even if a sub app's solve does not converge.

  • inactiveIf specified blocks matching these identifiers will be skipped.

    C++ Type:std::vector<std::string>

    Unit:(no unit assumed)

    Controllable:No

    Description:If specified blocks matching these identifiers will be skipped.

  • input_matrixSampling matrix for 'input-matrix' sampling.

    C++ Type:Eigen::Matrix<double, -1, -1, 0, -1, -1>

    Unit:(no unit assumed)

    Controllable:No

    Description:Sampling matrix for 'input-matrix' sampling.

  • linear_space_itemsParameter for defining the 'cartesian-prodcut' sampling scheme. A list of triplets, each item should include the min, step size, and number of steps.

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:Parameter for defining the 'cartesian-prodcut' sampling scheme. A list of triplets, each item should include the min, step size, and number of steps.

  • lognormal_locationThe 'lognormal' distributions' location parameter (m or mu).

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:The 'lognormal' distributions' location parameter (m or mu).

  • lognormal_scaleThe 'lognormal' distributions' scale parameter (s or sigma).

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:The 'lognormal' distributions' scale parameter (s or sigma).

  • min_procs_per_sample1Minimum number of processors to give to each sample. Useful for larger, distributed mesh solves where there are memory constraints.

    Default:1

    C++ Type:unsigned int

    Unit:(no unit assumed)

    Controllable:No

    Description:Minimum number of processors to give to each sample. Useful for larger, distributed mesh solves where there are memory constraints.

  • multiapp_modeThe operation mode, 'normal' creates one sub-application for each sample.'batch' creates one sub-app for each processor and re-executes for each local sample. 'reset' re-initializes the sub-app for every sample in the batch. 'restore' does not re-initialize and instead restores to first sample's initialization. 'keep-solution' re-uses the solution obtained from the first sample in the batch. 'no-restore' does not restore the sub-app.The default will be inferred based on the study.

    C++ Type:MooseEnum

    Unit:(no unit assumed)

    Options:normal, batch-reset, batch-restore, batch-keep-solution, batch-no-restore

    Controllable:No

    Description:The operation mode, 'normal' creates one sub-application for each sample.'batch' creates one sub-app for each processor and re-executes for each local sample. 'reset' re-initializes the sub-app for every sample in the batch. 'restore' does not re-initialize and instead restores to first sample's initialization. 'keep-solution' re-uses the solution obtained from the first sample in the batch. 'no-restore' does not restore the sub-app.The default will be inferred based on the study.

  • normal_meanMeans (or expectations) of the 'normal' distributions.

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:Means (or expectations) of the 'normal' distributions.

  • normal_standard_deviationStandard deviations of the 'normal' distributions.

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:Standard deviations of the 'normal' distributions.

  • num_samplesThe number of samples to generate for 'monte-carlo' and 'lhs' sampling.

    C++ Type:unsigned long

    Unit:(no unit assumed)

    Controllable:No

    Description:The number of samples to generate for 'monte-carlo' and 'lhs' sampling.

  • output_typejsonMethod in which to output sampler matrix and quantities of interest. Warning: 'csv' output will not include vector-type quantities.

    Default:json

    C++ Type:MultiMooseEnum

    Unit:(no unit assumed)

    Options:none, csv, json

    Controllable:No

    Description:Method in which to output sampler matrix and quantities of interest. Warning: 'csv' output will not include vector-type quantities.

  • quantities_of_interestList of the reporter names (object_name/value_name) that represent the quantities of interest for the study.

    C++ Type:std::vector<ReporterName>

    Unit:(no unit assumed)

    Controllable:No

    Description:List of the reporter names (object_name/value_name) that represent the quantities of interest for the study.

  • sampler_column_namesNames of the sampler columns for outputting the sampling matrix. If 'parameters' are not bracketed, the default is based on these values. Otherwise, the default is based on the sampler name.

    C++ Type:std::vector<ReporterValueName>

    Unit:(no unit assumed)

    Controllable:No

    Description:Names of the sampler columns for outputting the sampling matrix. If 'parameters' are not bracketed, the default is based on these values. Otherwise, the default is based on the sampler name.

  • seed0Random number generator initial seed

    Default:0

    C++ Type:unsigned int

    Unit:(no unit assumed)

    Controllable:No

    Description:Random number generator initial seed

  • show_study_objectsFalseSet to true to show all the objects being built by this action.

    Default:False

    C++ Type:bool

    Unit:(no unit assumed)

    Controllable:No

    Description:Set to true to show all the objects being built by this action.

  • statisticsmean stddevThe statistic(s) to compute for the study.

    Default:mean stddev

    C++ Type:MultiMooseEnum

    Unit:(no unit assumed)

    Options:min, max, sum, mean, stddev, norm2, ratio, stderr, median, meanabs

    Controllable:No

    Description:The statistic(s) to compute for the study.

  • tnormal_lower_bound'tnormal' distributions' lower bound

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:'tnormal' distributions' lower bound

  • tnormal_meanMeans (or expectations) of the 'tnormal' distributions.

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:Means (or expectations) of the 'tnormal' distributions.

  • tnormal_standard_deviationStandard deviations of the 'tnormal' distributions.

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:Standard deviations of the 'tnormal' distributions.

  • tnormal_upper_bound'tnormal' distributions' upper bound

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:'tnormal' distributions' upper bound

  • uniform_lower_boundLower bounds for 'uniform' distributions.

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:Lower bounds for 'uniform' distributions.

  • uniform_upper_boundUpper bounds 'uniform' distributions.

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:Upper bounds 'uniform' distributions.

  • weibull_locationLocation parameter (a or low) for 'weibull' distributions.

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:Location parameter (a or low) for 'weibull' distributions.

  • weibull_scaleScale parameter (b or lambda) for 'weibull' distributions.

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:Scale parameter (b or lambda) for 'weibull' distributions.

  • weibull_shapeShape parameter (c or k) for 'weibull' distributions.

    C++ Type:std::vector<double>

    Unit:(no unit assumed)

    Controllable:No

    Description:Shape parameter (c or k) for 'weibull' distributions.

Optional Parameters

  • control_tagsAdds user-defined labels for accessing object parameters via control logic.

    C++ Type:std::vector<std::string>

    Unit:(no unit assumed)

    Controllable:No

    Description:Adds user-defined labels for accessing object parameters via control logic.

Advanced Parameters