A reporter to support parallel active learning for Bayesian UQ tasks. More...

#include <BayesianActiveLearner.h>

Inheritance diagram for BayesianActiveLearner:

Public Types
typedef DataFileName	DataFileParameterType

Public Member Functions
	BayesianActiveLearner (const InputParameters &parameters)

virtual void	initialize () override

virtual void	finalize () override

virtual void	execute () override

void	threadJoin (const UserObject &) final

bool	shouldStore () const override final

virtual Real	spatialValue (const Point &) const

virtual const std::vector< Point >	spatialPoints () const

void	setPrimaryThreadCopy (UserObject *primary)

UserObject *	primaryThreadCopy ()

SubProblem &	getSubProblem () const

bool	shouldDuplicateInitialExecution () const

void	gatherSum (T &value)

void	gatherMax (T &value)

void	gatherMin (T &value)

void	gatherProxyValueMax (T1 &proxy, T2 &value)

void	gatherProxyValueMin (T1 &proxy, T2 &value)

std::set< UserObjectName >	getDependObjects () const

const std::set< std::string > &	getRequestedItems () override

const std::set< std::string > &	getSuppliedItems () override

unsigned int	systemNumber () const

virtual bool	needThreadedCopy () 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	haveParameter (const std::string &name) 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

virtual void	initialSetup ()

virtual void	timestepSetup ()

virtual void	jacobianSetup ()

virtual void	residualSetup ()

virtual void	customSetup (const ExecFlagType &)

const ExecFlagEnum &	getExecuteOnEnum () const

UserObjectName	getUserObjectName (const std::string &param_name) const

const T &	getUserObject (const std::string &param_name, bool is_dependency=true) const

const T &	getUserObjectByName (const UserObjectName &object_name, bool is_dependency=true) const

const UserObjectBase &	getUserObjectBase (const std::string &param_name, bool is_dependency=true) const

const UserObjectBase &	getUserObjectBaseByName (const UserObjectName &object_name, bool is_dependency=true) const

const std::vector< MooseVariableScalar *> &	getCoupledMooseScalarVars ()

const std::set< TagID > &	getScalarVariableCoupleableVectorTags () const

const std::set< TagID > &	getScalarVariableCoupleableMatrixTags () const

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialProperty (const std::string &name, MaterialData &material_data, const unsigned int state=0)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialProperty (const std::string &name, const unsigned int state=0)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialProperty (const std::string &name, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialProperty (const std::string &name, MaterialData &material_data, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialProperty (const std::string &name, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialProperty (const std::string &name, const unsigned int state=0)

const ADMaterialProperty< T > &	getADMaterialProperty (const std::string &name, MaterialData &material_data)

const ADMaterialProperty< T > &	getADMaterialProperty (const std::string &name)

const ADMaterialProperty< T > &	getADMaterialProperty (const std::string &name)

const MaterialProperty< T > &	getMaterialPropertyOld (const std::string &name, MaterialData &material_data)

const MaterialProperty< T > &	getMaterialPropertyOld (const std::string &name)

const MaterialProperty< T > &	getMaterialPropertyOld (const std::string &name)

const MaterialProperty< T > &	getMaterialPropertyOlder (const std::string &name, MaterialData &material_data)

const MaterialProperty< T > &	getMaterialPropertyOlder (const std::string &name)

const MaterialProperty< T > &	getMaterialPropertyOlder (const std::string &name)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialPropertyByName (const MaterialPropertyName &name, MaterialData &material_data, const unsigned int state)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialPropertyByName (const MaterialPropertyName &name, const unsigned int state=0)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialPropertyByName (const MaterialPropertyName &name, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialPropertyByName (const MaterialPropertyName &name, MaterialData &material_data, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialPropertyByName (const MaterialPropertyName &name, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialPropertyByName (const MaterialPropertyName &name, const unsigned int state=0)

const ADMaterialProperty< T > &	getADMaterialPropertyByName (const MaterialPropertyName &name, MaterialData &material_data)

const ADMaterialProperty< T > &	getADMaterialPropertyByName (const MaterialPropertyName &name)

const ADMaterialProperty< T > &	getADMaterialPropertyByName (const MaterialPropertyName &name)

const MaterialProperty< T > &	getMaterialPropertyOldByName (const MaterialPropertyName &name, MaterialData &material_data)

const MaterialProperty< T > &	getMaterialPropertyOldByName (const MaterialPropertyName &name)

const MaterialProperty< T > &	getMaterialPropertyOldByName (const MaterialPropertyName &name)

const MaterialProperty< T > &	getMaterialPropertyOlderByName (const MaterialPropertyName &name, MaterialData &material_data)

const MaterialProperty< T > &	getMaterialPropertyOlderByName (const MaterialPropertyName &name)

const MaterialProperty< T > &	getMaterialPropertyOlderByName (const MaterialPropertyName &name)

Moose::Kokkos::MaterialProperty< T, dimension >	getKokkosMaterialPropertyByName (const std::string &prop_name_in)

Moose::Kokkos::MaterialProperty< T, dimension >	getKokkosMaterialPropertyOldByName (const std::string &prop_name)

Moose::Kokkos::MaterialProperty< T, dimension >	getKokkosMaterialPropertyOlderByName (const std::string &prop_name)

Moose::Kokkos::MaterialProperty< T, dimension >	getKokkosMaterialProperty (const std::string &name)

Moose::Kokkos::MaterialProperty< T, dimension >	getKokkosMaterialPropertyOld (const std::string &name)

Moose::Kokkos::MaterialProperty< T, dimension >	getKokkosMaterialPropertyOlder (const std::string &name)

std::pair< const MaterialProperty< T > *, std::set< SubdomainID > >	getBlockMaterialProperty (const MaterialPropertyName &name)

std::pair< Moose::Kokkos::MaterialProperty< T, dimension >, std::set< SubdomainID > >	getKokkosBlockMaterialProperty (const MaterialPropertyName &name)

const GenericMaterialProperty< T, is_ad > &	getGenericZeroMaterialProperty (const std::string &name)

const GenericMaterialProperty< T, is_ad > &	getGenericZeroMaterialProperty ()

const GenericMaterialProperty< T, is_ad > &	getGenericZeroMaterialPropertyByName (const std::string &prop_name)

const MaterialProperty< T > &	getZeroMaterialProperty (Ts... args)

std::set< SubdomainID >	getMaterialPropertyBlocks (const std::string &name)

std::vector< SubdomainName >	getMaterialPropertyBlockNames (const std::string &name)

std::set< BoundaryID >	getMaterialPropertyBoundaryIDs (const std::string &name)

std::vector< BoundaryName >	getMaterialPropertyBoundaryNames (const std::string &name)

void	checkBlockAndBoundaryCompatibility (std::shared_ptr< MaterialBase > discrete)

std::unordered_map< SubdomainID, std::vector< MaterialBase *> >	buildRequiredMaterials (bool allow_stateful=true)

void	statefulPropertiesAllowed (bool)

virtual bool	getMaterialPropertyCalled () const

virtual const std::unordered_set< unsigned int > &	getMatPropDependencies () const

virtual void	resolveOptionalProperties ()

const GenericMaterialProperty< T, is_ad > &	getPossiblyConstantGenericMaterialPropertyByName (const MaterialPropertyName &prop_name, MaterialData &material_data, const unsigned int state)

bool	isImplicit ()

Moose::StateArg	determineState () const

virtual void	store (nlohmann::json &json) const

virtual void	declareLateValues ()

void	buildOutputHideVariableList (std::set< std::string > variable_names)

const std::set< OutputName > &	getOutputs ()

virtual void	subdomainSetup () override

virtual void	subdomainSetup () override

bool	hasUserObject (const std::string &param_name) const

bool	hasUserObject (const std::string &param_name) const

bool	hasUserObject (const std::string &param_name) const

bool	hasUserObject (const std::string &param_name) const

bool	hasUserObjectByName (const UserObjectName &object_name) const

bool	hasUserObjectByName (const UserObjectName &object_name) const

bool	hasUserObjectByName (const UserObjectName &object_name) const

bool	hasUserObjectByName (const UserObjectName &object_name) const

const GenericOptionalMaterialProperty< T, is_ad > &	getGenericOptionalMaterialProperty (const std::string &name, const unsigned int state=0)

const GenericOptionalMaterialProperty< T, is_ad > &	getGenericOptionalMaterialProperty (const std::string &name, const unsigned int state=0)

const OptionalMaterialProperty< T > &	getOptionalMaterialProperty (const std::string &name, const unsigned int state=0)

const OptionalMaterialProperty< T > &	getOptionalMaterialProperty (const std::string &name, const unsigned int state=0)

const OptionalADMaterialProperty< T > &	getOptionalADMaterialProperty (const std::string &name)

const OptionalADMaterialProperty< T > &	getOptionalADMaterialProperty (const std::string &name)

const OptionalMaterialProperty< T > &	getOptionalMaterialPropertyOld (const std::string &name)

const OptionalMaterialProperty< T > &	getOptionalMaterialPropertyOld (const std::string &name)

const OptionalMaterialProperty< T > &	getOptionalMaterialPropertyOlder (const std::string &name)

const OptionalMaterialProperty< T > &	getOptionalMaterialPropertyOlder (const std::string &name)

MaterialBase &	getMaterial (const std::string &name)

MaterialBase &	getMaterial (const std::string &name)

MaterialBase &	getMaterialByName (const std::string &name, bool no_warn=false)

MaterialBase &	getMaterialByName (const std::string &name, bool no_warn=false)

bool	hasMaterialProperty (const std::string &name)

bool	hasMaterialProperty (const std::string &name)

bool	hasMaterialPropertyByName (const std::string &name)

bool	hasMaterialPropertyByName (const std::string &name)

bool	hasADMaterialProperty (const std::string &name)

bool	hasADMaterialProperty (const std::string &name)

bool	hasADMaterialPropertyByName (const std::string &name)

bool	hasADMaterialPropertyByName (const std::string &name)

bool	hasKokkosMaterialProperty (const std::string &name)

bool	hasKokkosMaterialProperty (const std::string &name)

bool	hasKokkosMaterialPropertyByName (const std::string &name)

bool	hasKokkosMaterialPropertyByName (const std::string &name)

bool	hasGenericMaterialProperty (const std::string &name)

bool	hasGenericMaterialProperty (const std::string &name)

bool	hasGenericMaterialPropertyByName (const std::string &name)

bool	hasGenericMaterialPropertyByName (const std::string &name)

const Function &	getFunction (const std::string &name) const

const Function &	getFunctionByName (const FunctionName &name) const

bool	hasFunction (const std::string &param_name) const

bool	hasFunctionByName (const FunctionName &name) const

Moose::Kokkos::Function	getKokkosFunction (const std::string &name) const

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

Moose::Kokkos::Function	getKokkosFunctionByName (const FunctionName &name) const

const T &	getKokkosFunctionByName (const FunctionName &name) const

bool	hasKokkosFunction (const std::string &param_name) const

bool	hasKokkosFunctionByName (const FunctionName &name) const

bool	isDefaultPostprocessorValue (const std::string &param_name, const unsigned int index=0) const

bool	hasPostprocessor (const std::string &param_name, const unsigned int index=0) const

bool	hasPostprocessorByName (const PostprocessorName &name) const

std::size_t	coupledPostprocessors (const std::string &param_name) const

const PostprocessorName &	getPostprocessorName (const std::string &param_name, const unsigned int index=0) const

const VectorPostprocessorValue &	getVectorPostprocessorValue (const std::string &param_name, const std::string &vector_name) const

const VectorPostprocessorValue &	getVectorPostprocessorValue (const std::string &param_name, const std::string &vector_name, bool needs_broadcast) const

const VectorPostprocessorValue &	getVectorPostprocessorValueByName (const VectorPostprocessorName &name, const std::string &vector_name) const

const VectorPostprocessorValue &	getVectorPostprocessorValueByName (const VectorPostprocessorName &name, const std::string &vector_name, bool needs_broadcast) const

const VectorPostprocessorValue &	getVectorPostprocessorValueOld (const std::string &param_name, const std::string &vector_name) const

const VectorPostprocessorValue &	getVectorPostprocessorValueOld (const std::string &param_name, const std::string &vector_name, bool needs_broadcast) const

const VectorPostprocessorValue &	getVectorPostprocessorValueOldByName (const VectorPostprocessorName &name, const std::string &vector_name) const

const VectorPostprocessorValue &	getVectorPostprocessorValueOldByName (const VectorPostprocessorName &name, const std::string &vector_name, bool needs_broadcast) const

const ScatterVectorPostprocessorValue &	getScatterVectorPostprocessorValue (const std::string &param_name, const std::string &vector_name) const

const ScatterVectorPostprocessorValue &	getScatterVectorPostprocessorValueByName (const VectorPostprocessorName &name, const std::string &vector_name) const

const ScatterVectorPostprocessorValue &	getScatterVectorPostprocessorValueOld (const std::string &param_name, const std::string &vector_name) const

const ScatterVectorPostprocessorValue &	getScatterVectorPostprocessorValueOldByName (const VectorPostprocessorName &name, const std::string &vector_name) const

bool	hasVectorPostprocessor (const std::string &param_name, const std::string &vector_name) const

bool	hasVectorPostprocessor (const std::string &param_name) const

bool	hasVectorPostprocessorByName (const VectorPostprocessorName &name, const std::string &vector_name) const

bool	hasVectorPostprocessorByName (const VectorPostprocessorName &name) const

const VectorPostprocessorName &	getVectorPostprocessorName (const std::string &param_name) const

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

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

T &	getSamplerByName (const SamplerName &name)

Sampler &	getSamplerByName (const SamplerName &name)

virtual void	meshChanged ()

virtual void	meshDisplaced ()

PerfGraph &	perfGraph ()

const PostprocessorValue &	getPostprocessorValue (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValue (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValueOld (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValueOld (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValueOlder (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValueOlder (const std::string &param_name, const unsigned int index=0) const

virtual const PostprocessorValue &	getPostprocessorValueByName (const PostprocessorName &name) const

virtual const PostprocessorValue &	getPostprocessorValueByName (const PostprocessorName &name) const

const PostprocessorValue &	getPostprocessorValueOldByName (const PostprocessorName &name) const

const PostprocessorValue &	getPostprocessorValueOldByName (const PostprocessorName &name) const

const PostprocessorValue &	getPostprocessorValueOlderByName (const PostprocessorName &name) const

const PostprocessorValue &	getPostprocessorValueOlderByName (const PostprocessorName &name) const

bool	isVectorPostprocessorDistributed (const std::string &param_name) const

bool	isVectorPostprocessorDistributed (const std::string &param_name) const

bool	isVectorPostprocessorDistributedByName (const VectorPostprocessorName &name) const

bool	isVectorPostprocessorDistributedByName (const VectorPostprocessorName &name) const

const Distribution &	getDistribution (const std::string &name) const

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

const Distribution &	getDistribution (const std::string &name) const

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

const Distribution &	getDistributionByName (const DistributionName &name) const

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

const Distribution &	getDistributionByName (const DistributionName &name) const

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

const Parallel::Communicator &	comm () const

processor_id_type	n_processors () const

processor_id_type	processor_id () const

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


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

Static Public Member Functions
static InputParameters	validParams ()

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)

static void	sort (typename std::vector< T > &vector)

static void	sortDFS (typename std::vector< T > &vector)

static void	cyclicDependencyError (CyclicDependencyException< T2 > &e, const std::string &header, NameFunc &&name_func)

static void	cyclicDependencyError (CyclicDependencyException< T2 > &e, const std::string &header)

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

static constexpr PropertyValue::id_type	default_property_id

static constexpr PropertyValue::id_type	zero_property_id

static constexpr auto	SYSTEM

static constexpr auto	NAME

Protected Member Functions
virtual void	setupGPData (const std::vector< Real > &data_out, const DenseMatrix< Real > &data_in) override
	Sets up the training data for the GP model. More...

virtual Real	computeConvergenceValue () override
	Computes the convergence value during active learning. More...

virtual void	evaluateGPTest () override
	Evaluate the GP on all the test samples sent by the Sampler. More...

virtual void	includeAdditionalInputs () override
	Include additional inputs before evaluating the acquisition function. More...

virtual void	computeGPOutput (std::vector< Real > &eval_outputs)
	Computes the outputs of the trained GP model. More...

virtual void	setupGeneric ()
	Setup the generic variable for acquisition computation (depends on the objective: optimization, UQ, etc.) More...

virtual void	getAcquisition (std::vector< Real > &acq_new, std::vector< unsigned int > &indices)
	Output the acquisition function values and ordering of the indices. More...

virtual void	addPostprocessorDependencyHelper (const PostprocessorName &name) const override

virtual void	addVectorPostprocessorDependencyHelper (const VectorPostprocessorName &name) const override

virtual void	addUserObjectDependencyHelper (const UserObjectBase &uo) const override

void	addReporterDependencyHelper (const ReporterName &reporter_name) override

void	flagInvalidSolutionInternal (const InvalidSolutionID invalid_solution_id) const

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

const ReporterContextBase &	getReporterContextBaseByName (const ReporterName &reporter_name) const

const ReporterName &	getReporterName (const std::string &param_name) const

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

ManagedValue< T >	declareManagedRestartableDataWithContext (const std::string &data_name, void *context, Args &&... args)

const T &	getRestartableData (const std::string &data_name) const

T &	declareRestartableDataWithContext (const std::string &data_name, void *context, Args &&... args)

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

T &	declareRestartableDataWithObjectName (const std::string &data_name, const std::string &object_name, Args &&... args)

T &	declareRestartableDataWithObjectNameWithContext (const std::string &data_name, const std::string &object_name, void *context, Args &&... args)

std::string	restartableName (const std::string &data_name) const

const T &	getMeshProperty (const std::string &data_name, const std::string &prefix)

const T &	getMeshProperty (const std::string &data_name)

bool	hasMeshProperty (const std::string &data_name, const std::string &prefix) const

bool	hasMeshProperty (const std::string &data_name, const std::string &prefix) const

bool	hasMeshProperty (const std::string &data_name) const

bool	hasMeshProperty (const std::string &data_name) const

std::string	meshPropertyName (const std::string &data_name) const

PerfID	registerTimedSection (const std::string &section_name, const unsigned int level) const

PerfID	registerTimedSection (const std::string &section_name, const unsigned int level, const std::string &live_message, const bool print_dots=true) const

std::string	timedSectionName (const std::string &section_name) const

bool	isCoupledScalar (const std::string &var_name, unsigned int i=0) const

unsigned int	coupledScalarComponents (const std::string &var_name) const

unsigned int	coupledScalar (const std::string &var_name, unsigned int comp=0) const

libMesh::Order	coupledScalarOrder (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarValue (const std::string &var_name, unsigned int comp=0) const

const ADVariableValue &	adCoupledScalarValue (const std::string &var_name, unsigned int comp=0) const

const GenericVariableValue< is_ad > &	coupledGenericScalarValue (const std::string &var_name, unsigned int comp=0) const

const GenericVariableValue< false > &	coupledGenericScalarValue (const std::string &var_name, const unsigned int comp) const

const GenericVariableValue< true > &	coupledGenericScalarValue (const std::string &var_name, const unsigned int comp) const

const VariableValue &	coupledVectorTagScalarValue (const std::string &var_name, TagID tag, unsigned int comp=0) const

const VariableValue &	coupledMatrixTagScalarValue (const std::string &var_name, TagID tag, unsigned int comp=0) const

const VariableValue &	coupledScalarValueOld (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarValueOlder (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDot (const std::string &var_name, unsigned int comp=0) const

const ADVariableValue &	adCoupledScalarDot (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotDot (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotOld (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotDotOld (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotDu (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotDotDu (const std::string &var_name, unsigned int comp=0) const

const MooseVariableScalar *	getScalarVar (const std::string &var_name, unsigned int comp) const

virtual void	checkMaterialProperty (const std::string &name, const unsigned int state)

virtual void	getKokkosMaterialPropertyHook (const std::string &, const unsigned int)

void	markMatPropRequested (const std::string &)

MaterialPropertyName	getMaterialPropertyName (const std::string &name) const

void	checkExecutionStage ()

T &	declareUnusedValue (Args &&... args)

const T &	getReporterValue (const std::string &param_name, const std::size_t time_index=0)

const T &	getReporterValue (const std::string &param_name, ReporterMode mode, const std::size_t time_index=0)

const T &	getReporterValue (const std::string &param_name, const std::size_t time_index=0)

const T &	getReporterValue (const std::string &param_name, ReporterMode mode, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, ReporterMode mode, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, ReporterMode mode, const std::size_t time_index=0)

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

const GenericMaterialProperty< T, is_ad > *	defaultGenericMaterialProperty (const std::string &name)

const GenericMaterialProperty< T, is_ad > *	defaultGenericMaterialProperty (const std::string &name)

const MaterialProperty< T > *	defaultMaterialProperty (const std::string &name)

const MaterialProperty< T > *	defaultMaterialProperty (const std::string &name)

const ADMaterialProperty< T > *	defaultADMaterialProperty (const std::string &name)

const ADMaterialProperty< T > *	defaultADMaterialProperty (const std::string &name)

T &	declareValue (const std::string &param_name, Args &&... args)

T &	declareValue (const std::string &param_name, ReporterMode mode, Args &&... args)

T &	declareValue (const std::string &param_name, Args &&... args)

T &	declareValue (const std::string &param_name, ReporterMode mode, Args &&... args)

T &	declareValue (const std::string &param_name, Args &&... args)

T &	declareValue (const std::string &param_name, ReporterMode mode, Args &&... args)

T &	declareValue (const std::string &param_name, Args &&... args)

T &	declareValue (const std::string &param_name, ReporterMode mode, Args &&... args)

T &	declareValueByName (const ReporterValueName &value_name, Args &&... args)

T &	declareValueByName (const ReporterValueName &value_name, ReporterMode mode, Args &&... args)

T &	declareValueByName (const ReporterValueName &value_name, Args &&... args)

T &	declareValueByName (const ReporterValueName &value_name, ReporterMode mode, Args &&... args)

T &	declareValueByName (const ReporterValueName &value_name, Args &&... args)

T &	declareValueByName (const ReporterValueName &value_name, ReporterMode mode, Args &&... args)

T &	declareValueByName (const ReporterValueName &value_name, Args &&... args)

T &	declareValueByName (const ReporterValueName &value_name, ReporterMode mode, Args &&... args)

ParallelAcquisitionFunctionBase &	getParallelAcquisitionFunctionByName (const UserObjectName &name) const
	Lookup a ParallelAcquisitionFunction object by name and return pointer. More...

LikelihoodFunctionBase *	getLikelihoodFunctionByName (const UserObjectName &name) const
	Lookup a LikelihoodFunction object by name and return pointer. More...

Static Protected Member Functions
static std::string	meshPropertyName (const std::string &data_name, const std::string &prefix)

Protected Attributes
BayesianActiveLearningSampler &	_al_sampler
	The base sampler. More...

unsigned int	_n_dim
	The input dimension for GP, equal to Sampler columns. More...

dof_id_type	_props
	Storage for the number of parallel proposals. More...

const std::vector< std::vector< Real > > &	_inputs_test
	Storage for all the proposed samples to test the GP model. More...

const std::vector< Real > &	_output_value
	Model output value from SubApp. More...

std::vector< Real > &	_output_comm
	Modified value of model output by this reporter class. More...

std::vector< unsigned int > &	_sorted_indices
	The selected sample indices to evaluate the subApp. More...

const ActiveLearningGaussianProcess &	_al_gp
	The active learning GP trainer that permits re-training. More...

const SurrogateModel &	_gp_eval
	The GP evaluator object that permits re-evaluations. More...

ParallelAcquisitionFunctionBase &	_acquisition_obj
	Storage for the parallel acquisition object to be utilized. More...

std::vector< Real > &	_acquisition_value
	The acquistion function values in the current iteration. More...

Real &	_convergence_value
	For monitoring convergence of active learning. More...

std::vector< std::vector< Real > >	_inputs_test_modified
	Storage for all the modified proposed samples to test the GP model. More...

std::vector< std::vector< Real > > &	_inputs_required
	Transmit the required inputs to the json file. More...

const bool &	_penalize_acquisition
	Penalize acquisition to prevent clustering when operating in parallel. More...

int	_check_step
	Ensure that the MCMC algorithm proceeds in a sequential fashion. More...

std::vector< std::vector< Real > >	_gp_inputs
	Storage for the GP re-training inputs. More...

std::vector< Real >	_gp_outputs
	Storage for the GP re-training outputs. More...

std::vector< Real >	_gp_outputs_test
	Outputs of GP model for the test samples. More...

std::vector< Real >	_gp_std_test
	Outputs of GP model standard deviation for the test samples. More...

std::vector< Real >	_length_scales
	Storage for the length scales after the GP training. More...

std::vector< Real >	_generic
	A generic parameter to be passed to the acquisition function. More...

std::vector< Real >	_eval_outputs_current
	The GP outputs from the current iteration before re-training (to evaluate convergence) More...

const Moose::CoordinateSystemType &	_coord_sys

const THREAD_ID	_tid

SubProblem &	_subproblem

FEProblemBase &	_fe_problem

SystemBase &	_sys

Assembly &	_assembly

const bool	_duplicate_initial_execution

std::set< std::string >	_depend_uo

const bool &	_enabled

MooseApp &	_app

Factory &	_factory

ActionFactory &	_action_factory

const std::string &	_type

const std::string &	_name

const InputParameters &	_pars

const ExecFlagEnum &	_execute_enum

const ExecFlagType &	_current_execute_flag

MooseApp &	_restartable_app

const std::string	_restartable_system_name

const THREAD_ID	_restartable_tid

const bool	_restartable_read_only

FEProblemBase &	_mci_feproblem

FEProblemBase &	_mdi_feproblem

MooseApp &	_pg_moose_app

const std::string	_prefix

FEProblemBase &	_sc_fe_problem

const THREAD_ID	_sc_tid

const Real &	_real_zero

const VariableValue &	_scalar_zero

const Point &	_point_zero

const InputParameters &	_mi_params

const std::string	_mi_name

const MooseObjectName	_mi_moose_object_name

FEProblemBase &	_mi_feproblem

SubProblem &	_mi_subproblem

const THREAD_ID	_mi_tid

const bool	_is_kokkos_object

const Moose::MaterialDataType	_material_data_type

MaterialData &	_material_data

bool	_stateful_allowed

bool	_get_material_property_called

std::vector< std::unique_ptr< PropertyValue > >	_default_properties

std::unordered_set< unsigned int >	_material_property_dependencies

const MaterialPropertyName	_get_suffix

const bool	_use_interpolated_state

const InputParameters &	_ti_params

FEProblemBase &	_ti_feproblem

bool	_is_implicit

Real &	_t

const Real &	_t_old

int &	_t_step

Real &	_dt

Real &	_dt_old

bool	_is_transient

const Parallel::Communicator &	_communicator

Static Protected Attributes
static const std::string	_interpolated_old

static const std::string	_interpolated_older

Private Member Functions
void	computeLogLikelihood (const std::vector< Real > &data_out)
	Sets up the training data for the GP model for Bayesian UQ tasks. More...

Private Attributes
const std::vector< Real > &	_new_var_samples
	Storage for new proposed variance samples. More...

const Distribution *	_var_prior
	Storage for the prior over the variance. More...

const std::vector< Real > &	_var_test
	Storage for all the proposed variance samples to test the GP model. More...

Real &	_noise
	Model noise term to pass to Likelihoods object. More...

std::vector< const LikelihoodFunctionBase * >	_likelihoods
	Storage for the likelihood objects to be utilized. More...

dof_id_type	_num_confg_values
	Storage for the number of experimental configuration values. More...

dof_id_type	_num_confg_params
	Storage for the number of experimental configuration parameters. More...

std::vector< Real >	_log_likelihood
	Storage for the computed log-likelihood values in each iteration of active learning. More...

unsigned int	_n_dim_plus_var
	The input dimension for GP for Bayesian problems with var, equal to Sampler columns + 1. More...

Detailed Description

A reporter to support parallel active learning for Bayesian UQ tasks.

Definition at line 20 of file BayesianActiveLearner.h.

Constructor & Destructor Documentation

◆ BayesianActiveLearner()

BayesianActiveLearner::BayesianActiveLearner ( const InputParameters & parameters )

Definition at line 27 of file BayesianActiveLearner.C.

   : GenericActiveLearnerTempl<BayesianActiveLearningSampler>(parameters),
     LikelihoodInterface(parameters),
     _new_var_samples(_al_sampler.getVarSamples()),
     _var_prior(_al_sampler.getVarPrior()),
     _var_test(_al_sampler.getVarSampleTries()),
     _noise(declareValue<Real>("noise"))
 {
   // Filling the `likelihoods` vector with the user-provided distributions.
   for (const UserObjectName & name : getParam<std::vector<UserObjectName>>("likelihoods"))
     _likelihoods.push_back(getLikelihoodFunctionByName(name));
 
   _num_confg_values = _al_sampler.getNumberOfConfigValues();
   _num_confg_params = _al_sampler.getNumberOfConfigParams();
 
   // Resize the length scales depending upon whether variance is included
   _n_dim = _al_sampler.getNumberOfCols() - _al_sampler.getNumberOfConfigParams();
   _n_dim_plus_var = _n_dim + 1;
   if (_var_prior)
     _length_scales.resize(_n_dim_plus_var);
   else
     _length_scales.resize(_n_dim);
 
   // Resize the log-likelihood vector to the number of parallel proposals
   _log_likelihood.resize(_props);
 }

Member Function Documentation

◆ computeConvergenceValue()

Real BayesianActiveLearner::computeConvergenceValue ( )

overrideprotectedvirtual

Computes the convergence value during active learning.

Reimplemented from GenericActiveLearnerTempl< BayesianActiveLearningSampler >.

Definition at line 98 of file BayesianActiveLearner.C.

 {
   Real convergence_value = 0.0;
   unsigned int num_valid = 0;
   for (unsigned int ii = 0; ii < _props; ++ii)
   {
     if (!std::isnan(_log_likelihood[ii]))
     {
       convergence_value += Utility::pow<2>(_log_likelihood[ii] - _eval_outputs_current[ii]);
       ++num_valid;
     }
   }
   convergence_value = std::sqrt(convergence_value) / num_valid;
   return convergence_value;
 }

◆ computeGPOutput()

void GenericActiveLearnerTempl< BayesianActiveLearningSampler >::computeGPOutput ( std::vector< Real > & eval_outputs )

protectedvirtualinherited

Computes the outputs of the trained GP model.

Parameters

eval_outputs The outputs predicted by the GP model

Definition at line 245 of file GenericActiveLearner.h.

 {
   for (unsigned int i = 0; i < eval_outputs.size(); ++i)
     eval_outputs[i] = _gp_eval.evaluate(_gp_inputs[i]);
 }

◆ computeLogLikelihood()

void BayesianActiveLearner::computeLogLikelihood ( const std::vector< Real > & data_out )

private

Sets up the training data for the GP model for Bayesian UQ tasks.

Parameters

log_likelihood	The log-likelihood to be computed
data_out	The data vector containing the outputs from subApp evaluations

Definition at line 79 of file BayesianActiveLearner.C.

Referenced by setupGPData().

 {
   _log_likelihood.assign(_props, 0.0);
   std::vector<Real> out1(_num_confg_values);
   for (unsigned int i = 0; i < _props; ++i)
   {
     for (unsigned int j = 0; j < _num_confg_values; ++j)
       out1[j] = data_out[j * _props + i];
     if (_var_prior)
     {
       _noise = std::sqrt(_new_var_samples[i]);
       _log_likelihood[i] += _likelihoods[0]->function(out1);
     }
     else
       _log_likelihood[i] += _likelihoods[0]->function(out1);
   }
 }

◆ evaluateGPTest()

void BayesianActiveLearner::evaluateGPTest ( )

overrideprotectedvirtual

Evaluate the GP on all the test samples sent by the Sampler.

Reimplemented from GenericActiveLearnerTempl< BayesianActiveLearningSampler >.

Definition at line 115 of file BayesianActiveLearner.C.

 {
   std::vector<Real> tmp;
   if (_var_prior)
     tmp.resize(_n_dim_plus_var);
   else
     tmp.resize(_n_dim);
   for (unsigned int i = 0; i < _gp_outputs_test.size(); ++i)
   {
     std::copy(_inputs_test[i].begin(), _inputs_test[i].end(), tmp.begin());
     if (_var_prior)
       tmp[_n_dim] = _var_test[i];
     _gp_outputs_test[i] = _gp_eval.evaluate(tmp, _gp_std_test[i]);
   }
 }

◆ execute()

void GenericActiveLearnerTempl< BayesianActiveLearningSampler >::execute ( )

overridevirtualinherited

Implements GeneralReporter.

Definition at line 302 of file GenericActiveLearner.h.

 {
   if (_al_sampler.getNumberOfLocalRows() == 0 || _check_step == _t_step)
   {
     _check_step = _t_step;
     return;
   }
 
   DenseMatrix<Real> data_in(_al_sampler.getNumberOfRows(), _al_sampler.getNumberOfCols());
   for (dof_id_type ss = _al_sampler.getLocalRowBegin(); ss < _al_sampler.getLocalRowEnd(); ++ss)
   {
     const auto data = _al_sampler.getNextLocalRow();
     for (unsigned int j = 0; j < _al_sampler.getNumberOfCols(); ++j)
       data_in(ss, j) = data[j];
   }
   _communicator.sum(data_in.get_values());
   _output_comm = _output_value;
   _communicator.allgather(_output_comm);
 
   if (_t_step > 1)
   {
     // Setup the GP training data
     setupGPData(_output_comm, data_in);
 
     // Compute the convergence value before re-training the GP
     if (_t_step > 2)
     {
       computeGPOutput(_eval_outputs_current);
       _convergence_value = computeConvergenceValue();
     }
 
     // Retrain the GP and get the length scales
     _al_gp.reTrain(_gp_inputs, _gp_outputs);
     _length_scales = _al_gp.getLengthScales();
 
     // Evaluate the GP on all the test samples sent by the Sampler
     evaluateGPTest();
 
     // Setup the generic variable for acquisition computation (depends on the objective:
     // optimization, UQ, etc.)
     setupGeneric();
 
     // Get the acquisition function values and ordering of indices as per the acquisition
     std::vector<Real> acq_new;
     std::vector<unsigned int> indices;
     indices.resize(_inputs_test.size());
     getAcquisition(acq_new, indices);
 
     // Output the acquisition function values and the best ordering of the indices
     std::copy_n(indices.begin(), _props, _sorted_indices.begin());
     std::copy_n(acq_new.begin(), _props, _acquisition_value.begin());
   }
   else
     std::iota(_sorted_indices.begin(), _sorted_indices.end(), 0);
 
   // Track the current step
   _check_step = _t_step;
 }

◆ finalize()

virtual void GenericActiveLearnerTempl< BayesianActiveLearningSampler >::finalize ( )

inlineoverridevirtualinherited

Implements GeneralReporter.

Definition at line 42 of file GenericActiveLearner.h.

42 {}

◆ getAcquisition()

void GenericActiveLearnerTempl< BayesianActiveLearningSampler >::getAcquisition	(	std::vector< Real > &	acq_new,
		std::vector< unsigned int > &	indices
	)

protectedvirtualinherited

Output the acquisition function values and ordering of the indices.

Parameters

acq_new	The computed values of the acquisition function
indices	The indices ordered according to the acqusition values to be sent to Sampler

Definition at line 267 of file GenericActiveLearner.h.

 {
   std::vector<Real> acq;
   acq.resize(_inputs_test.size());
   includeAdditionalInputs();
   _acquisition_obj.computeAcquisition(
       acq, _gp_outputs_test, _gp_std_test, _inputs_test_modified, _gp_inputs, _generic);
   acq_new = acq;
   if (_penalize_acquisition)
     _acquisition_obj.penalizeAcquisition(
         acq_new, indices, acq, _length_scales, _inputs_test_modified);
 }

◆ getLikelihoodFunctionByName()

LikelihoodFunctionBase * LikelihoodInterface::getLikelihoodFunctionByName ( const UserObjectName & name ) const

protectedinherited

Lookup a LikelihoodFunction object by name and return pointer.

Definition at line 24 of file LikelihoodInterface.C.

Referenced by BayesianActiveLearner(), PMCMCDecision::PMCMCDecision(), and TestLikelihood::TestLikelihood().

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

◆ getParallelAcquisitionFunctionByName()

ParallelAcquisitionFunctionBase & ParallelAcquisitionInterface::getParallelAcquisitionFunctionByName ( const UserObjectName & name ) const

protectedinherited

Lookup a ParallelAcquisitionFunction object by name and return pointer.

Definition at line 24 of file ParallelAcquisitionInterface.C.

 {
   std::vector<ParallelAcquisitionFunctionBase *> models;
   _parallelacquisition_feproblem.theWarehouse()
       .query()
       .condition<AttribName>(name)
       .condition<AttribSystem>("ParallelAcquisitionFunctionBase")
       .queryInto(models);
 
   if (models.empty())
     mooseError("Unable to find a ParallelAcquisitionFunction object with the name '" + name + "'");
 
   return *models.front();
 }

◆ 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;
 }

◆ includeAdditionalInputs()

void BayesianActiveLearner::includeAdditionalInputs ( )

overrideprotectedvirtual

Include additional inputs before evaluating the acquisition function.

Has trivial function in base, but can be modified in derived if necessary depending upon the objective of active learning (i.e., forward UQ, inverse UQ, optimization, etc.)

Reimplemented from GenericActiveLearnerTempl< BayesianActiveLearningSampler >.

Definition at line 132 of file BayesianActiveLearner.C.

 {
   _inputs_test_modified = _inputs_test;
   if (_var_prior)
     for (unsigned int i = 0; i < _inputs_test.size(); ++i)
       _inputs_test_modified[i].push_back(_var_test[i]);
 }

◆ initialize()

virtual void GenericActiveLearnerTempl< BayesianActiveLearningSampler >::initialize ( )

inlineoverridevirtualinherited

Implements GeneralReporter.

Definition at line 41 of file GenericActiveLearner.h.

41 {}

◆ setupGeneric()

void GenericActiveLearnerTempl< BayesianActiveLearningSampler >::setupGeneric ( )

protectedvirtualinherited

Setup the generic variable for acquisition computation (depends on the objective: optimization, UQ, etc.)

Definition at line 253 of file GenericActiveLearner.h.

 {
   _generic = _gp_outputs;
 }

◆ setupGPData()

void BayesianActiveLearner::setupGPData	(	const std::vector< Real > &	data_out,
		const DenseMatrix< Real > &	data_in
	)

overrideprotectedvirtual

Sets up the training data for the GP model.

Parameters

data_out	The data vector containing the outputs to train the GP
data_in	The data matrix containing the inputs to train the GP

Reimplemented from GenericActiveLearnerTempl< BayesianActiveLearningSampler >.

Definition at line 55 of file BayesianActiveLearner.C.

 {
   std::vector<Real> tmp;
   computeLogLikelihood(data_out);
   if (_var_prior)
     tmp.resize(_n_dim_plus_var);
   else
     tmp.resize(_n_dim);
   for (unsigned int i = 0; i < _props; ++i)
   {
     for (unsigned int j = 0; j < _n_dim; ++j)
       tmp[j] = data_in(i, j);
     if (_var_prior)
       tmp[_n_dim] = _new_var_samples[i];
     if (!std::isnan(_log_likelihood[i]))
     {
       _gp_inputs.push_back(tmp);
       _gp_outputs.push_back(_log_likelihood[i]);
     }
   }
 }

◆ validParams()

InputParameters BayesianActiveLearner::validParams ( )

static

Definition at line 15 of file BayesianActiveLearner.C.

 {
   InputParameters params = GenericActiveLearner::validParams();
   params += LikelihoodInterface::validParams();
   params.addClassDescription(
       "A reporter to support parallel active learning for Bayesian UQ tasks.");
   params.addRequiredParam<std::vector<UserObjectName>>("likelihoods", "Names of likelihoods.");
   params.addParam<ReporterValueName>(
       "noise", "noise", "Name of the model noise term to pass to Likelihoods object.");
   return params;
 }

Member Data Documentation

◆ _acquisition_obj

ParallelAcquisitionFunctionBase& GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_acquisition_obj

protectedinherited

Storage for the parallel acquisition object to be utilized.

Definition at line 117 of file GenericActiveLearner.h.

◆ _acquisition_value

std::vector<Real>& GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_acquisition_value

protectedinherited

The acquistion function values in the current iteration.

Definition at line 120 of file GenericActiveLearner.h.

◆ _al_gp

const ActiveLearningGaussianProcess& GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_al_gp

protectedinherited

The active learning GP trainer that permits re-training.

Definition at line 111 of file GenericActiveLearner.h.

◆ _al_sampler

BayesianActiveLearningSampler & GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_al_sampler

protectedinherited

The base sampler.

Definition at line 90 of file GenericActiveLearner.h.

Referenced by BayesianActiveLearner().

◆ _check_step

int GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_check_step

protectedinherited

Ensure that the MCMC algorithm proceeds in a sequential fashion.

Definition at line 135 of file GenericActiveLearner.h.

◆ _convergence_value

Real& GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_convergence_value

protectedinherited

For monitoring convergence of active learning.

Definition at line 123 of file GenericActiveLearner.h.

◆ _eval_outputs_current

std::vector<Real> GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_eval_outputs_current

protectedinherited

The GP outputs from the current iteration before re-training (to evaluate convergence)

Definition at line 156 of file GenericActiveLearner.h.

Referenced by computeConvergenceValue().

◆ _generic

std::vector<Real> GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_generic

protectedinherited

A generic parameter to be passed to the acquisition function.

Definition at line 153 of file GenericActiveLearner.h.

◆ _gp_eval

const SurrogateModel& GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_gp_eval

protectedinherited

The GP evaluator object that permits re-evaluations.

Definition at line 114 of file GenericActiveLearner.h.

Referenced by evaluateGPTest().

◆ _gp_inputs

std::vector<std::vector<Real> > GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_gp_inputs

protectedinherited

Storage for the GP re-training inputs.

Definition at line 138 of file GenericActiveLearner.h.

Referenced by setupGPData().

◆ _gp_outputs

std::vector<Real> GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_gp_outputs

protectedinherited

Storage for the GP re-training outputs.

Definition at line 141 of file GenericActiveLearner.h.

Referenced by setupGPData().

◆ _gp_outputs_test

std::vector<Real> GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_gp_outputs_test

protectedinherited

Outputs of GP model for the test samples.

Definition at line 144 of file GenericActiveLearner.h.

Referenced by evaluateGPTest().

◆ _gp_std_test

std::vector<Real> GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_gp_std_test

protectedinherited

Outputs of GP model standard deviation for the test samples.

Definition at line 147 of file GenericActiveLearner.h.

Referenced by evaluateGPTest().

◆ _inputs_required

std::vector<std::vector<Real> >& GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_inputs_required

protectedinherited

Transmit the required inputs to the json file.

Definition at line 129 of file GenericActiveLearner.h.

◆ _inputs_test

const std::vector<std::vector<Real> >& GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_inputs_test

protectedinherited

Storage for all the proposed samples to test the GP model.

Definition at line 99 of file GenericActiveLearner.h.

Referenced by evaluateGPTest(), and includeAdditionalInputs().

◆ _inputs_test_modified

std::vector<std::vector<Real> > GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_inputs_test_modified

protectedinherited

Storage for all the modified proposed samples to test the GP model.

Definition at line 126 of file GenericActiveLearner.h.

Referenced by includeAdditionalInputs().

◆ _length_scales

std::vector<Real> GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_length_scales

protectedinherited

Storage for the length scales after the GP training.

Definition at line 150 of file GenericActiveLearner.h.

Referenced by BayesianActiveLearner().

◆ _likelihoods

std::vector<const LikelihoodFunctionBase *> BayesianActiveLearner::_likelihoods

private

Storage for the likelihood objects to be utilized.

Definition at line 60 of file BayesianActiveLearner.h.

Referenced by BayesianActiveLearner(), and computeLogLikelihood().

◆ _log_likelihood

std::vector<Real> BayesianActiveLearner::_log_likelihood

private

Storage for the computed log-likelihood values in each iteration of active learning.

Definition at line 69 of file BayesianActiveLearner.h.

Referenced by BayesianActiveLearner(), computeConvergenceValue(), computeLogLikelihood(), and setupGPData().

◆ _n_dim

unsigned int GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_n_dim

protectedinherited

The input dimension for GP, equal to Sampler columns.

Definition at line 93 of file GenericActiveLearner.h.

Referenced by BayesianActiveLearner(), evaluateGPTest(), and setupGPData().

◆ _n_dim_plus_var

unsigned int BayesianActiveLearner::_n_dim_plus_var

private

The input dimension for GP for Bayesian problems with var, equal to Sampler columns + 1.

Definition at line 72 of file BayesianActiveLearner.h.

Referenced by BayesianActiveLearner(), evaluateGPTest(), and setupGPData().

◆ _new_var_samples

const std::vector<Real>& BayesianActiveLearner::_new_var_samples

private

Storage for new proposed variance samples.

Definition at line 48 of file BayesianActiveLearner.h.

Referenced by computeLogLikelihood(), and setupGPData().

◆ _noise

Real& BayesianActiveLearner::_noise

private

Model noise term to pass to Likelihoods object.

Definition at line 57 of file BayesianActiveLearner.h.

Referenced by computeLogLikelihood().

◆ _num_confg_params

dof_id_type BayesianActiveLearner::_num_confg_params

private

Storage for the number of experimental configuration parameters.

Definition at line 66 of file BayesianActiveLearner.h.

Referenced by BayesianActiveLearner().

◆ _num_confg_values

dof_id_type BayesianActiveLearner::_num_confg_values

private

Storage for the number of experimental configuration values.

Definition at line 63 of file BayesianActiveLearner.h.

Referenced by BayesianActiveLearner(), and computeLogLikelihood().

◆ _output_comm

std::vector<Real>& GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_output_comm

protectedinherited

Modified value of model output by this reporter class.

Definition at line 105 of file GenericActiveLearner.h.

◆ _output_value

const std::vector<Real>& GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_output_value

protectedinherited

Model output value from SubApp.

Definition at line 102 of file GenericActiveLearner.h.

◆ _penalize_acquisition

const bool& GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_penalize_acquisition

protectedinherited

Penalize acquisition to prevent clustering when operating in parallel.

Definition at line 132 of file GenericActiveLearner.h.

◆ _props

dof_id_type GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_props

protectedinherited

Storage for the number of parallel proposals.

Definition at line 96 of file GenericActiveLearner.h.

Referenced by BayesianActiveLearner(), computeConvergenceValue(), computeLogLikelihood(), and setupGPData().

◆ _sorted_indices

std::vector<unsigned int>& GenericActiveLearnerTempl< BayesianActiveLearningSampler >::_sorted_indices

protectedinherited

The selected sample indices to evaluate the subApp.

Definition at line 108 of file GenericActiveLearner.h.

◆ _var_prior

const Distribution* BayesianActiveLearner::_var_prior

private

Storage for the prior over the variance.

Definition at line 51 of file BayesianActiveLearner.h.

Referenced by BayesianActiveLearner(), computeLogLikelihood(), evaluateGPTest(), includeAdditionalInputs(), and setupGPData().

◆ _var_test

const std::vector<Real>& BayesianActiveLearner::_var_test

private

Storage for all the proposed variance samples to test the GP model.

Definition at line 54 of file BayesianActiveLearner.h.

Referenced by evaluateGPTest(), and includeAdditionalInputs().

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

stochastic_tools/include/reporters/BayesianActiveLearner.h
stochastic_tools/src/reporters/BayesianActiveLearner.C

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Static Public Attributes

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Static Protected Attributes

Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ BayesianActiveLearner()

Member Function Documentation

◆ computeConvergenceValue()

◆ computeGPOutput()

◆ computeLogLikelihood()

◆ evaluateGPTest()

◆ execute()

◆ finalize()

◆ getAcquisition()

◆ getLikelihoodFunctionByName()

◆ getParallelAcquisitionFunctionByName()

◆ getSurrogateModel() [1/2]

◆ getSurrogateModel() [2/2]

◆ getSurrogateModelByName() [1/2]

◆ getSurrogateModelByName() [2/2]

◆ getSurrogateTrainer() [1/2]

◆ getSurrogateTrainer() [2/2]

◆ getSurrogateTrainerByName() [1/2]

◆ getSurrogateTrainerByName() [2/2]

◆ includeAdditionalInputs()

◆ initialize()

◆ setupGeneric()

◆ setupGPData()

◆ validParams()

Member Data Documentation

◆ _acquisition_obj

◆ _acquisition_value

◆ _al_gp

◆ _al_sampler

◆ _check_step

◆ _convergence_value

◆ _eval_outputs_current

◆ _generic

◆ _gp_eval

◆ _gp_inputs

◆ _gp_outputs

◆ _gp_outputs_test

◆ _gp_std_test

◆ _inputs_required

◆ _inputs_test

◆ _inputs_test_modified

◆ _length_scales

◆ _likelihoods

◆ _log_likelihood

◆ _n_dim

◆ _n_dim_plus_var

◆ _new_var_samples

◆ _noise

◆ _num_confg_params

◆ _num_confg_values

◆ _output_comm

◆ _output_value

◆ _penalize_acquisition

◆ _props

◆ _sorted_indices

◆ _var_prior

◆ _var_test