#include <GaussianProcessSurrogate.h>

Inheritance diagram for GaussianProcessSurrogate:

Public Types
typedef DataFileName	DataFileParameterType

Public Member Functions
	GaussianProcessSurrogate (const InputParameters &parameters)

virtual Real	evaluate (const std::vector< Real > &x) const
	Evaluate surrogate model given a row of parameters. More...

virtual void	evaluate (const std::vector< Real > &x, std::vector< Real > &y) const
	Various evaluate methods that can be overriden. More...

virtual Real	evaluate (const std::vector< Real > &x, Real &std) const
	Evaluate methods that also return predicted standard deviation (see GaussianProcess.h) More...

virtual void	evaluate (const std::vector< Real > &x, std::vector< Real > &y, std::vector< Real > &std) const

virtual void	setupCovariance (UserObjectName _covar_name)
	This function is called by LoadCovarianceDataAction when the surrogate is loading training data from a file. More...

StochasticTools::GaussianProcess &	gp ()

const StochasticTools::GaussianProcess &	getGP () const

virtual Real	evaluate (const std::vector< Real > &x) const
	Evaluate surrogate model given a row of parameters. More...

virtual void	evaluate (const std::vector< Real > &x, std::vector< Real > &y) const
	Various evaluate methods that can be overriden. More...

virtual Real	evaluate (const std::vector< Real > &x, Real &std) const
	Evaluate methods that also return predicted standard deviation (see GaussianProcess.h) More...

virtual void	evaluate (const std::vector< Real > &x, std::vector< Real > &y, std::vector< Real > &) const

virtual bool	enabled () const

std::shared_ptr< MooseObject >	getSharedPtr ()

std::shared_ptr< const MooseObject >	getSharedPtr () const

MooseApp &	getMooseApp () const

const std::string &	type () const

virtual const std::string &	name () const

std::string	typeAndName () const

std::string	errorPrefix (const std::string &error_type) const

void	callMooseError (std::string msg, const bool with_prefix) const

MooseObjectParameterName	uniqueParameterName (const std::string &parameter_name) const

const InputParameters &	parameters () const

MooseObjectName	uniqueName () const

const T &	getParam (const std::string &name) const

std::vector< std::pair< T1, T2 > >	getParam (const std::string &param1, const std::string &param2) const

const T *	queryParam (const std::string &name) const

const T &	getRenamedParam (const std::string &old_name, const std::string &new_name) const

T	getCheckedPointerParam (const std::string &name, const std::string &error_string="") const

bool	isParamValid (const std::string &name) const

bool	isParamSetByUser (const std::string &nm) const

void	paramError (const std::string &param, Args... args) const

void	paramWarning (const std::string &param, Args... args) const

void	paramInfo (const std::string &param, Args... args) const

void	connectControllableParams (const std::string &parameter, const std::string &object_type, const std::string &object_name, const std::string &object_parameter) const

void	mooseError (Args &&... args) const

void	mooseErrorNonPrefixed (Args &&... args) const

void	mooseDocumentedError (const std::string &repo_name, const unsigned int issue_num, Args &&... args) const

void	mooseWarning (Args &&... args) const

void	mooseWarningNonPrefixed (Args &&... args) const

void	mooseDeprecated (Args &&... args) const

void	mooseInfo (Args &&... args) const

std::string	getDataFileName (const std::string &param) const

std::string	getDataFileNameByName (const std::string &relative_path) const

std::string	getDataFilePath (const std::string &relative_path) const

const Parallel::Communicator &	comm () const

processor_id_type	n_processors () const

processor_id_type	processor_id () const

T &	getSampler (const std::string &name)

Sampler &	getSampler (const std::string &name)

T &	getSamplerByName (const SamplerName &name)

Sampler &	getSamplerByName (const SamplerName &name)

template<>
SurrogateModel &	getSurrogateModel (const std::string &name) const

template<>
SurrogateTrainerBase &	getSurrogateTrainer (const std::string &name) const

template<>
SurrogateModel &	getSurrogateModelByName (const UserObjectName &name) const

template<>
SurrogateTrainerBase &	getSurrogateTrainerByName (const UserObjectName &name) const

const std::string &	modelMetaDataName () const
	Accessor for the name of the model meta data. More...

const FileName &	getModelDataFileName () const
	Get the associated filename. More...

bool	hasModelData () const
	Check if we need to load model data (if the filename parameter is used) More...


template<typename T = SurrogateModel>
T &	getSurrogateModel (const std::string &name) const
	Get a SurrogateModel/Trainer with a given name. More...

template<typename T = SurrogateTrainerBase>
T &	getSurrogateTrainer (const std::string &name) const


template<typename T = SurrogateModel>
T &	getSurrogateModelByName (const UserObjectName &name) const
	Get a sampler with a given name. More...

template<typename T = SurrogateTrainerBase>
T &	getSurrogateTrainerByName (const UserObjectName &name) const


template<typename T , typename... Args>
T &	declareModelData (const std::string &data_name, Args &&... args)
	Declare model data for loading from file as well as restart. More...


template<typename T , typename... Args>
const T &	getModelData (const std::string &data_name, Args &&... args) const
	Retrieve model data from the interface. More...

Static Public Member Functions
static InputParameters	validParams ()

static MooseEnum	defaultPredictorTypes ()

static MooseEnum	defaultResponseTypes ()

Public Attributes
const ConsoleStream	_console

Protected Member Functions
CovarianceFunctionBase *	getCovarianceFunctionByName (const UserObjectName &name) const
	Lookup a CovarianceFunction object by name and return pointer. More...

Protected Attributes
const bool &	_enabled

MooseApp &	_app

const std::string	_type

const std::string	_name

const InputParameters &	_pars

Factory &	_factory

ActionFactory &	_action_factory

const Parallel::Communicator &	_communicator

Private Attributes
StochasticTools::GaussianProcess &	_gp

const RealEigenMatrix &	_training_params
	Paramaters (x) used for training. More...

Detailed Description

Definition at line 18 of file GaussianProcessSurrogate.h.

Constructor & Destructor Documentation

◆ GaussianProcessSurrogate()

GaussianProcessSurrogate::GaussianProcessSurrogate ( const InputParameters & parameters )

Definition at line 25 of file GaussianProcessSurrogate.C.

   : SurrogateModel(parameters),
     CovarianceInterface(parameters),
     _gp(declareModelData<StochasticTools::GaussianProcess>("_gp")),
     _training_params(getModelData<RealEigenMatrix>("_training_params"))
 {
 }

Member Function Documentation

◆ declareModelData()

template<typename T , typename... Args>

T & RestartableModelInterface::declareModelData	(	const std::string &	data_name,
		Args &&...	args
	)

inherited

Declare model data for loading from file as well as restart.

Definition at line 78 of file RestartableModelInterface.h.

 {
   return _model_restartable.declareRestartableData<T>(data_name, std::forward<Args>(args)...);
 }

◆ defaultPredictorTypes()

static MooseEnum SurrogateModel::defaultPredictorTypes ( )

inlinestaticinherited

Definition at line 27 of file SurrogateModel.h.

27 { return MooseEnum("real"); }

MooseEnum

◆ defaultResponseTypes()

static MooseEnum SurrogateModel::defaultResponseTypes ( )

inlinestaticinherited

Definition at line 28 of file SurrogateModel.h.

Referenced by EvaluateSurrogate::validParams().

28 { return MooseEnum("real vector_real"); }

MooseEnum

◆ evaluate() [1/8]

virtual void SurrogateModel::evaluate

inline

Various evaluate methods that can be overriden.

Definition at line 43 of file SurrogateModel.h.

   {
     evaluateError(x, y);
   }

◆ evaluate() [2/8]

virtual Real SurrogateModel::evaluate

inline

Evaluate methods that also return predicted standard deviation (see GaussianProcess.h)

Definition at line 53 of file SurrogateModel.h.

   {
     evaluateError(x, std, true);
     return 0.0;
   }

◆ evaluate() [3/8]

virtual void SurrogateModel::evaluate

inline

Definition at line 59 of file SurrogateModel.h.

   {
     evaluateError(x, y, true);
   }

◆ evaluate() [4/8]

virtual Real SurrogateModel::evaluate

inline

Evaluate surrogate model given a row of parameters.

Definition at line 33 of file SurrogateModel.h.

   {
     evaluateError(x, Real());
     return 0.0;
   };

◆ evaluate() [5/8]

Real GaussianProcessSurrogate::evaluate ( const std::vector< Real > & x ) const

virtual

Evaluate surrogate model given a row of parameters.

Reimplemented from SurrogateModel.

Definition at line 42 of file GaussianProcessSurrogate.C.

Referenced by evaluate().

 {
   // Overlaod for evaluate to maintain general compatibility. Only returns mean
   Real dummy = 0;
   return this->evaluate(x, dummy);
 }

◆ evaluate() [6/8]

void GaussianProcessSurrogate::evaluate	(	const std::vector< Real > &	x,
		std::vector< Real > &	y
	)		const

virtual

Various evaluate methods that can be overriden.

Reimplemented from SurrogateModel.

Definition at line 60 of file GaussianProcessSurrogate.C.

 {
   // Overlaod for evaluate to maintain general compatibility. Only returns mean
   std::vector<Real> std_dummy;
   this->evaluate(x, y, std_dummy);
 }

◆ evaluate() [7/8]

Real GaussianProcessSurrogate::evaluate	(	const std::vector< Real > &	x,
		Real &	std
	)		const

virtual

Evaluate methods that also return predicted standard deviation (see GaussianProcess.h)

Reimplemented from SurrogateModel.

Definition at line 50 of file GaussianProcessSurrogate.C.

 {
   std::vector<Real> y;
   std::vector<Real> std;
   this->evaluate(x, y, std);
   std_dev = std[0];
   return y[0];
 }

◆ evaluate() [8/8]

void GaussianProcessSurrogate::evaluate	(	const std::vector< Real > &	x,
		std::vector< Real > &	y,
		std::vector< Real > &	std
	)		const

virtual

Reimplemented from SurrogateModel.

Definition at line 68 of file GaussianProcessSurrogate.C.

 {
   const unsigned int n_dims = _training_params.cols();
 
   mooseAssert(x.size() == n_dims,
               "Number of parameters provided for evaluation does not match number of parameters "
               "used for training.");
   const unsigned int n_outputs = _gp.getCovarFunction().numOutputs();
 
   y = std::vector<Real>(n_outputs, 0.0);
   std = std::vector<Real>(n_outputs, 0.0);
 
   RealEigenMatrix test_points(1, n_dims);
   for (unsigned int ii = 0; ii < n_dims; ++ii)
     test_points(0, ii) = x[ii];
 
   _gp.getParamStandardizer().getStandardized(test_points);
 
   RealEigenMatrix K_train_test(_training_params.rows() * n_outputs, n_outputs);
 
   _gp.getCovarFunction().computeCovarianceMatrix(
       K_train_test, _training_params, test_points, false);
   RealEigenMatrix K_test(n_outputs, n_outputs);
   _gp.getCovarFunction().computeCovarianceMatrix(K_test, test_points, test_points, true);
 
   // Compute the predicted mean value (centered)
   RealEigenMatrix pred_value = (K_train_test.transpose() * _gp.getKResultsSolve()).transpose();
   // De-center/scale the value and store for return
   _gp.getDataStandardizer().getDestandardized(pred_value);
 
   RealEigenMatrix pred_var =
       K_test - (K_train_test.transpose() * _gp.getKCholeskyDecomp().solve(K_train_test));
 
   // Vairance computed, take sqrt for standard deviation, scale up by training data std and store
   RealEigenMatrix std_dev_mat = pred_var.array().sqrt();
   _gp.getDataStandardizer().getDescaled(std_dev_mat);
 
   for (const auto output_i : make_range(n_outputs))
   {
     y[output_i] = pred_value(0, output_i);
     std[output_i] = std_dev_mat(output_i, output_i);
   }
 }

◆ getCovarianceFunctionByName()

CovarianceFunctionBase * CovarianceInterface::getCovarianceFunctionByName ( const UserObjectName & name ) const

protectedinherited

Lookup a CovarianceFunction object by name and return pointer.

Definition at line 25 of file CovarianceInterface.C.

Referenced by ActiveLearningGaussianProcess::ActiveLearningGaussianProcess(), CovarianceFunctionBase::CovarianceFunctionBase(), GaussianProcessTrainer::GaussianProcessTrainer(), and setupCovariance().

 {
   std::vector<CovarianceFunctionBase *> models;
   _covar_feproblem.theWarehouse()
       .query()
       .condition<AttribName>(name)
       .condition<AttribSystem>("CovarianceFunction")
       .queryInto(models);
   if (models.empty())
     mooseError("Unable to find a CovarianceFunction object with the name '" + name + "'");
   return models[0];
 }

◆ getGP()

const StochasticTools::GaussianProcess& GaussianProcessSurrogate::getGP ( ) const

inline

Definition at line 38 of file GaussianProcessSurrogate.h.

Referenced by GaussianProcessData::initialize().

38 { return _gp; }

GaussianProcessSurrogate::_gp

StochasticTools::GaussianProcess & _gp

Definition: GaussianProcessSurrogate.h:41

◆ getModelData()

template<typename T , typename... Args>

const T & RestartableModelInterface::getModelData	(	const std::string &	data_name,
		Args &&...	args
	)		const

inherited

Retrieve model data from the interface.

Definition at line 85 of file RestartableModelInterface.h.

 {
   return _model_restartable.getRestartableData<T>(data_name, std::forward<Args>(args)...);
 }

◆ getModelDataFileName()

const FileName & RestartableModelInterface::getModelDataFileName ( ) const

inherited

Get the associated filename.

Definition at line 33 of file RestartableModelInterface.C.

 {
   return _model_object.getParam<FileName>("filename");
 }

◆ getSurrogateModel() [1/2]

template<>

SurrogateModel& SurrogateModelInterface::getSurrogateModel ( const std::string & name ) const

inherited

Definition at line 46 of file SurrogateModelInterface.C.

 {
   return getSurrogateModelByName<SurrogateModel>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateModel() [2/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateModel ( const std::string & name ) const

inherited

Get a SurrogateModel/Trainer with a given name.

Parameters

name	The name of the parameter key of the sampler to retrieve

Returns: The sampler with name associated with the parameter 'name'

Definition at line 81 of file SurrogateModelInterface.h.

Referenced by SurrogateTrainer::initialize().

 {
   return getSurrogateModelByName<T>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateModelByName() [1/2]

template<>

SurrogateModel& SurrogateModelInterface::getSurrogateModelByName ( const UserObjectName & name ) const

inherited

Definition at line 31 of file SurrogateModelInterface.C.

 {
   std::vector<SurrogateModel *> models;
   _smi_feproblem.theWarehouse()
       .query()
       .condition<AttribName>(name)
       .condition<AttribSystem>("SurrogateModel")
       .queryInto(models);
   if (models.empty())
     mooseError("Unable to find a SurrogateModel object with the name '" + name + "'");
   return *(models[0]);
 }

◆ getSurrogateModelByName() [2/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateModelByName ( const UserObjectName & name ) const

inherited

Get a sampler with a given name.

Parameters

name	The name of the sampler to retrieve

Returns: The sampler with name 'name'

Definition at line 88 of file SurrogateModelInterface.h.

Referenced by CrossValidationScores::CrossValidationScores(), EvaluateSurrogate::EvaluateSurrogate(), and InverseMapping::initialSetup().

 {
   std::vector<T *> models;
   _smi_feproblem.theWarehouse()
       .query()
       .condition<AttribName>(name)
       .condition<AttribSystem>("SurrogateModel")
       .queryInto(models);
   if (models.empty())
     mooseError("Unable to find a SurrogateModel object of type " + std::string(typeid(T).name()) +
                " with the name '" + name + "'");
   return *(models[0]);
 }

◆ getSurrogateTrainer() [1/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateTrainer ( const std::string & name ) const

inherited

Definition at line 104 of file SurrogateModelInterface.h.

 {
   return getSurrogateTrainerByName<T>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateTrainer() [2/2]

template<>

SurrogateTrainerBase& SurrogateModelInterface::getSurrogateTrainer ( const std::string & name ) const

inherited

Definition at line 60 of file SurrogateModelInterface.C.

 {
   return getSurrogateTrainerByName<SurrogateTrainerBase>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateTrainerByName() [1/2]

template<>

SurrogateTrainerBase& SurrogateModelInterface::getSurrogateTrainerByName ( const UserObjectName & name ) const

inherited

Definition at line 53 of file SurrogateModelInterface.C.

 {
   return _smi_feproblem.getUserObject<SurrogateTrainerBase>(name);
 }

◆ getSurrogateTrainerByName() [2/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateTrainerByName ( const UserObjectName & name ) const

inherited

Definition at line 111 of file SurrogateModelInterface.h.

Referenced by SurrogateTrainerOutput::output().

 {
   SurrogateTrainerBase * base_ptr =
       &_smi_feproblem.getUserObject<SurrogateTrainerBase>(name, _smi_tid);
   T * obj_ptr = dynamic_cast<T *>(base_ptr);
   if (!obj_ptr)
     mooseError("Failed to find a SurrogateTrainer object of type " + std::string(typeid(T).name()) +
                    " with the name '",
                name,
                "' for the desired type.");
   return *obj_ptr;
 }

◆ gp()

StochasticTools::GaussianProcess& GaussianProcessSurrogate::gp ( )

inline

Definition at line 37 of file GaussianProcessSurrogate.h.

37 { return _gp; }

GaussianProcessSurrogate::_gp

StochasticTools::GaussianProcess & _gp

Definition: GaussianProcessSurrogate.h:41

◆ hasModelData()

bool RestartableModelInterface::hasModelData ( ) const

inherited

Check if we need to load model data (if the filename parameter is used)

Definition at line 39 of file RestartableModelInterface.C.

 {
   return _model_object.isParamValid("filename");
 }

◆ modelMetaDataName()

const std::string& RestartableModelInterface::modelMetaDataName ( ) const

inlineinherited

Accessor for the name of the model meta data.

Definition at line 47 of file RestartableModelInterface.h.

Referenced by SurrogateTrainerOutput::output(), and MappingOutput::output().

47 { return _model_meta_data_name; }

RestartableModelInterface::_model_meta_data_name

const std::string _model_meta_data_name

The model meta data name.

Definition: RestartableModelInterface.h:61

◆ setupCovariance()

void GaussianProcessSurrogate::setupCovariance ( UserObjectName _covar_name )

virtual

This function is called by LoadCovarianceDataAction when the surrogate is loading training data from a file.

The action must recreate the covariance object before this surrogate can set the correct pointer.

Definition at line 34 of file GaussianProcessSurrogate.C.

 {
   if (_gp.getCovarFunctionPtr() != nullptr)
     ::mooseError("Attempting to redefine covariance function using setupCovariance.");
   _gp.linkCovarianceFunction(getCovarianceFunctionByName(covar_name));
 }

◆ validParams()

InputParameters GaussianProcessSurrogate::validParams ( )

static

Definition at line 18 of file GaussianProcessSurrogate.C.

 {
   InputParameters params = SurrogateModel::validParams();
   params.addClassDescription("Computes and evaluates Gaussian Process surrogate model.");
   return params;
 }

Member Data Documentation

◆ _gp

StochasticTools::GaussianProcess& GaussianProcessSurrogate::_gp

private

Definition at line 41 of file GaussianProcessSurrogate.h.

Referenced by evaluate(), getGP(), gp(), and setupCovariance().

◆ _training_params

const RealEigenMatrix& GaussianProcessSurrogate::_training_params

private

Paramaters (x) used for training.

Definition at line 44 of file GaussianProcessSurrogate.h.

Referenced by evaluate().

The documentation for this class was generated from the following files:

stochastic_tools/include/surrogates/GaussianProcessSurrogate.h
stochastic_tools/src/surrogates/GaussianProcessSurrogate.C

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ GaussianProcessSurrogate()

Member Function Documentation

◆ declareModelData()

◆ defaultPredictorTypes()

◆ defaultResponseTypes()

◆ evaluate() [1/8]

◆ evaluate() [2/8]

◆ evaluate() [3/8]

◆ evaluate() [4/8]

◆ evaluate() [5/8]

◆ evaluate() [6/8]

◆ evaluate() [7/8]

◆ evaluate() [8/8]

◆ getCovarianceFunctionByName()

◆ getGP()

◆ getModelData()

◆ getModelDataFileName()

◆ getSurrogateModel() [1/2]

◆ getSurrogateModel() [2/2]

◆ getSurrogateModelByName() [1/2]

◆ getSurrogateModelByName() [2/2]

◆ getSurrogateTrainer() [1/2]

◆ getSurrogateTrainer() [2/2]

◆ getSurrogateTrainerByName() [1/2]

◆ getSurrogateTrainerByName() [2/2]

◆ gp()

◆ hasModelData()

◆ modelMetaDataName()

◆ setupCovariance()

◆ validParams()

Member Data Documentation

◆ _gp

◆ _training_params