#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.

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

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

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.

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.

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

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

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

bool	isKokkosObject () const

MooseApp &	getMooseApp () const

const std::string &	type () const

const std::string &	name () const

std::string	typeAndName () const

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

MooseObjectName	uniqueName () const

const InputParameters &	parameters () const

const hit::Node *	getHitNode () const

bool	hasBase () const

const std::string &	getBase () 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 &name) const

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

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

void	paramWarning (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

std::string	messagePrefix (const bool hit_prefix=true) const

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

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

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

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

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

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

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

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

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

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

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

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

void	callMooseError (std::string msg, const bool with_prefix, const hit::Node *node=nullptr, const bool show_trace=true) 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.

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

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


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

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.

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.


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

Static Public Member Functions
static InputParameters	validParams ()

static MooseEnum	defaultPredictorTypes ()

static MooseEnum	defaultResponseTypes ()

static void	callMooseError (MooseApp const app, const InputParameters &params, std::string msg, const bool with_prefix, const hit::Node node, const bool show_trace=true)

Public Attributes
	usingCombinedWarningSolutionWarnings

const ConsoleStream	_console

Static Public Attributes
static const std::string	type_param

static const std::string	name_param

static const std::string	unique_name_param

static const std::string	app_param

static const std::string	moose_base_param

static const std::string	kokkos_object_param

Protected Member Functions
void	flagInvalidSolutionInternal (const InvalidSolutionID invalid_solution_id) const

InvalidSolutionID	registerInvalidSolutionInternal (const std::string &message, const bool warning) const

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

Protected Attributes
const bool &	_enabled

MooseApp &	_app

Factory &	_factory

ActionFactory &	_action_factory

const std::string &	_type

const std::string &	_name

const InputParameters &	_pars

const Parallel::Communicator &	_communicator

Private Member Functions
template<typename P , typename R >
void	evaluateError (P x, R y, bool with_std=false) const

Static Private Member Functions
static const hit::Node *	getHitNode (const InputParameters &params)

static std::string	messagePrefix (const InputParameters &params, const bool hit_prefix)

Private Attributes
StochasticTools::GaussianProcess &	_gp

const torch::Tensor &	_training_params
	Paramaters (x) used for training.

const ParallelParamObject &	_parent

const MooseBase &	_si_moose_base

const FEProblemBase *	_si_problem

const InputParameters &	_si_params

FEProblemBase &	_si_feproblem

THREAD_ID	_si_tid

const InputParameters &	_smi_params
	Parameters of the object with this interface.

FEProblemBase &	_smi_feproblem
	Reference to FEProblemBase instance.

const THREAD_ID	_smi_tid
	Thread ID.

const MooseObject &	_model_object
	Reference to the MooseObject that uses this interface.

const std::string	_model_meta_data_name
	The model meta data name.

PublicRestartable	_model_restartable
	Member for interfacing with the framework's restartable system.

FEProblemBase &	_covar_feproblem
	Reference to FEProblemBase instance.

Detailed Description

Definition at line 20 of file GaussianProcessSurrogate.h.

Constructor & Destructor Documentation

◆ GaussianProcessSurrogate()

GaussianProcessSurrogate::GaussianProcessSurrogate ( const InputParameters & parameters )

Definition at line 26 of file GaussianProcessSurrogate.C.

  : SurrogateModel(parameters),
    CovarianceInterface(parameters),
    _gp(declareModelData<StochasticTools::GaussianProcess>("_gp")),
    _training_params(getModelData<torch::Tensor>("_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 73 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.

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

Referenced by EvaluateSurrogate::validParams().

◆ evaluate() [1/8]

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

inlinevirtual

Evaluate surrogate model given a row of parameters.

Reimplemented from SurrogateModel.

Definition at line 33 of file SurrogateModel.h.

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

◆ evaluate() [2/8]

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

virtual

Evaluate surrogate model given a row of parameters.

Reimplemented from SurrogateModel.

Definition at line 43 of file GaussianProcessSurrogate.C.

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

Referenced by evaluate(), evaluate(), and evaluate().

◆ evaluate() [3/8]

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

inlinevirtual

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

Reimplemented from SurrogateModel.

Definition at line 53 of file SurrogateModel.h.

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

◆ evaluate() [4/8]

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 51 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() [5/8]

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

inlinevirtual

Various evaluate methods that can be overriden.

Reimplemented from SurrogateModel.

Definition at line 43 of file SurrogateModel.h.

  {
    evaluateError(x, y);
  }

◆ 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 61 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]

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

inlinevirtual

Reimplemented from SurrogateModel.

Definition at line 59 of file SurrogateModel.h.

  {
    evaluateError(x, y, true);
  }

◆ 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 69 of file GaussianProcessSurrogate.C.

{
  const unsigned int n_dims = _training_params.sizes()[1];
 
  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);
 
  const auto options = _training_params.options().dtype(at::kDouble);
 
  torch::Tensor test_points = torch::empty({1, n_dims}, at::kDouble);
  auto points_accessor = test_points.accessor<Real, 2>();
  for (unsigned int ii = 0; ii < n_dims; ++ii)
    points_accessor[0][ii] = x[ii];
  test_points = test_points.to(options.device());
 
  _gp.getParamStandardizer().getStandardized(test_points);
 
  torch::Tensor K_train_test =
      torch::empty({_training_params.sizes()[0] * n_outputs, n_outputs}, options);
 
  _gp.getCovarFunction().computeCovarianceMatrix(
      K_train_test, _training_params, test_points, false);
  torch::Tensor K_test = torch::empty({n_outputs, n_outputs}, options);
  _gp.getCovarFunction().computeCovarianceMatrix(K_test, test_points, test_points, true);
 
  //  Compute the predicted mean value (centered)
  torch::Tensor pred_value = torch::transpose(
      torch::mm(torch::transpose(K_train_test, 0, 1), _gp.getKResultsSolve()), 0, 1);
 
  //   De-center/scale the value and store for return
  _gp.getDataStandardizer().getDestandardized(pred_value);
 
  torch::Tensor pred_var =
      K_test - torch::mm(torch::transpose(K_train_test, 0, 1),
                         torch::cholesky_solve(K_train_test, _gp.getKCholeskyDecomp()));
 
  // Only the marginal variances are returned. Clamp tiny negative roundoff before sqrt.
  torch::Tensor std_dev_vec =
      torch::sqrt(torch::clamp_min(torch::diagonal(pred_var), 0.0)).unsqueeze(0);
  _gp.getDataStandardizer().getDescaled(std_dev_vec);
  const auto std_dev_cpu = LibtorchUtils::toCPUContiguous(std_dev_vec);
  const auto pred_value_cpu = LibtorchUtils::toCPUContiguous(pred_value);
  auto std_accessor = std_dev_cpu.accessor<Real, 2>();
  auto pred_value_accessor = pred_value_cpu.accessor<Real, 2>();
 
  for (const auto output_i : make_range(n_outputs))
  {
    y[output_i] = pred_value_accessor[0][output_i];
    std[output_i] = std_accessor[0][output_i];
  }
}

◆ evaluateError()

template<typename P , typename R >

void SurrogateModel::evaluateError	(	P	x,
		R	y,
		bool	with_std = `false`
	)		const

privateinherited

Definition at line 72 of file SurrogateModel.h.

{
  std::stringstream ss;
  ss << "Evaluate method";
  if (with_std)
    ss << " (including standard deviation computation)";
  ss << " with predictor type " << MooseUtils::prettyCppType<P>();
  ss << " and response type " << MooseUtils::prettyCppType<R>();
  ss << " has not been implemented.";
  mooseError(ss.str());
}

Referenced by SurrogateModel::evaluate(), SurrogateModel::evaluate(), SurrogateModel::evaluate(), and SurrogateModel::evaluate().

◆ getCovarianceFunctionByName()

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

protectedinherited

Lookup a CovarianceFunction object by name and return pointer.

Definition at line 26 of file CovarianceInterface.C.

{
  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];
}

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

◆ getGP()

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

inline

Definition at line 40 of file GaussianProcessSurrogate.h.

40{ return _gp; }

Referenced by GaussianProcessData::initialize().

◆ 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 80 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<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.

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

Referenced by SurrogateTrainer::initialize().

◆ getSurrogateModel() [2/2]

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));
}

◆ getSurrogateModelByName() [1/2]

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.

{
  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]);
}

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

◆ getSurrogateModelByName() [2/2]

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]);
}

◆ 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<typename T >

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

inherited

Definition at line 111 of file SurrogateModelInterface.h.

{
  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;
}

Referenced by SurrogateTrainerOutput::output().

◆ getSurrogateTrainerByName() [2/2]

template<>

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

inherited

Definition at line 53 of file SurrogateModelInterface.C.

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

◆ gp()

StochasticTools::GaussianProcess & GaussianProcessSurrogate::gp ( )

inline

Definition at line 39 of file GaussianProcessSurrogate.h.

39{ return _gp; }

◆ 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.

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

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

◆ 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 35 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 19 of file GaussianProcessSurrogate.C.

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

Member Data Documentation

◆ _covar_feproblem

FEProblemBase& CovarianceInterface::_covar_feproblem

privateinherited

Reference to FEProblemBase instance.

Definition at line 31 of file CovarianceInterface.h.

Referenced by CovarianceInterface::getCovarianceFunctionByName().

◆ _gp

StochasticTools::GaussianProcess& GaussianProcessSurrogate::_gp

private

Definition at line 43 of file GaussianProcessSurrogate.h.

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

◆ _model_meta_data_name

const std::string RestartableModelInterface::_model_meta_data_name

privateinherited

The model meta data name.

This is used to store the restartable data within the RestartableDataMap.

Definition at line 61 of file RestartableModelInterface.h.

Referenced by RestartableModelInterface::modelMetaDataName(), and RestartableModelInterface::RestartableModelInterface().

◆ _model_object

const MooseObject& RestartableModelInterface::_model_object

privateinherited

Reference to the MooseObject that uses this interface.

Definition at line 57 of file RestartableModelInterface.h.

Referenced by RestartableModelInterface::getModelDataFileName(), RestartableModelInterface::hasModelData(), and RestartableModelInterface::RestartableModelInterface().

◆ _model_restartable

PublicRestartable RestartableModelInterface::_model_restartable

privateinherited

Member for interfacing with the framework's restartable system.

We need this because we would like to have the capability to handle the model data separately from the other data members used for checkpointing.

Definition at line 68 of file RestartableModelInterface.h.

Referenced by RestartableModelInterface::declareModelData(), and RestartableModelInterface::getModelData().

◆ _smi_feproblem

FEProblemBase& SurrogateModelInterface::_smi_feproblem

privateinherited

Reference to FEProblemBase instance.

Definition at line 73 of file SurrogateModelInterface.h.

Referenced by SurrogateModelInterface::getSurrogateModelByName(), and SurrogateModelInterface::getSurrogateTrainerByName().

◆ _smi_params

const InputParameters& SurrogateModelInterface::_smi_params

privateinherited

Parameters of the object with this interface.

Definition at line 70 of file SurrogateModelInterface.h.

Referenced by SurrogateModelInterface::getSurrogateModel(), and SurrogateModelInterface::getSurrogateTrainer().

◆ _smi_tid

const THREAD_ID SurrogateModelInterface::_smi_tid

privateinherited

Thread ID.

Definition at line 76 of file SurrogateModelInterface.h.

Referenced by SurrogateModelInterface::getSurrogateTrainerByName().

◆ _training_params

const torch::Tensor& GaussianProcessSurrogate::_training_params

private

Paramaters (x) used for training.

Definition at line 46 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

Static Public Attributes

Protected Member Functions

Protected Attributes

Private Member Functions

Static Private Member Functions

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]

◆ evaluateError()

◆ 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

◆ _covar_feproblem

◆ _gp

◆ _model_meta_data_name

◆ _model_object

◆ _model_restartable

◆ _smi_feproblem

◆ _smi_params

◆ _smi_tid

◆ _training_params