- filenameThe name of the file which will be associated with the saved/loaded data.
C++ Type:FileName
Unit:(no unit assumed)
Controllable:No
Description:The name of the file which will be associated with the saved/loaded data.
- trainerThe SurrogateTrainer object. If this is specified the trainer data is automatically gathered and available in this SurrogateModel object.
C++ Type:UserObjectName
Unit:(no unit assumed)
Controllable:No
Description:The SurrogateTrainer object. If this is specified the trainer data is automatically gathered and available in this SurrogateModel object.
PolynomialRegressionSurrogate
Evaluates polynomial regression model with coefficients computed from PolynomialRegressionTrainer.
Overview
This surrogate model takes the vector containing model coefficients () from PolynomialRegressionTrainer and computes the value of the QoI at a new parameter sample by simply evaluating
(1)where denotes the coordinates of the new sample in the parameter space. It is important to mention that unlike NearestPointSurrogate, this surrogate model does not require the evaluation of a function (e.g. distance) for all training points to determine the new value at . Thus, for large training data bases, using a PolynomialRegressionSurrogate
for repeated runs is faster.
Example Input File Syntax
To create a surrogate model which uses polynomial regression, one can use the following syntax:
(contrib/moose/modules/stochastic_tools/test/tests/surrogates/polynomial_regression/evaluate.i)It is visible that the data from PolynomialRegressionTrainer has been saved to train_out_train.rd
and the surrogate model is constructed by loading the necessary information from it. If one wants to do the training and evaluation in the same input, the following syntax can be used:
where train
is the ID of the PolynomialRegressionTrainer object. For the sampling of the surrogate model, the same objects can be used in the Samplers
block:
Finally, a reporter of type EvaluateSurrogate is created to extract the approximate value of the QoI(s):
(contrib/moose/modules/stochastic_tools/test/tests/surrogates/polynomial_regression/evaluate.i)Input 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.
- enableTrueSet the enabled status of the MooseObject.
Default:True
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Set the enabled status of the MooseObject.