Body class for model specification data. More...

Public Member Functions
	~DataModelRep ()
	destructor

Public Attributes
String	idModel
	string identifier for the model specification data set (from the `id_model` specification in ModelIndControl)

String	modelType
	model type selection: single, surrogate, or nested (from the model type specification in ModelIndControl)

String	variablesPointer
	string pointer to the variables specification to be used by this model (from the `variables_pointer` specification in ModelIndControl)

String	interfacePointer
	string pointer to the interface specification to be used by this model (from the `interface_pointer` specification in ModelSingle and the `optional_interface_pointer` specification in ModelNested)

String	responsesPointer
	string pointer to the responses specification to be used by this model (from the `responses_pointer` specification in ModelIndControl)

bool	hierarchicalTags
	whether this model and its children will add hierarchy-based tags to eval ids

String	subMethodPointer
	pointer to a sub-iterator used for global approximations (from the `dace_method_pointer` specification in ModelSurrG) or by nested models (from the `sub_method_pointer` specification in ModelNested)

String	solutionLevelControl
	(state) variable identifier that defines a set or range of solution level controls (space/time discretization levels, iterative convergence tolerances, etc.) for defining a secondary hierarchy of fidelity within the scope of a single model form (from `solution_level_control` specification; see also `ordered_model_fidelities`)

RealVector	solutionLevelCost
	array of relative simulation costs corresponding to each of the solution levels (from `solution_level_cost` specification; see also `solution_level_control`); a scalar input is interpreted as a constant cost multiplier to be applied recursively

String	costRecoveryMetadata
	identifier for response metadata that returns the incurred cost of a simulation execution. This online recovery option (typically averaged over a pilot sample) can replace the need for a priori specification of solutionLevelCost.

SizetSet	surrogateFnIndices
	array specifying the response function set that is approximated

String	surrogateType
	the selected surrogate type: local_taylor, multipoint_tana, global_(neural_network,mars,orthogonal_polynomial,gaussian, polynomial,kriging), or hierarchical

String	truthModelPointer
	pointer to the model specification for constructing the truth model used in constructing surrogates

StringArray	ensembleModelPointers
	an ordered (low to high) or unordered (peer) set of model pointers corresponding to a ensemble of modeling fidelities (from the `ordered_model_fidelities` specification in ModelSurrH or the `unordered_model_fidelities` specification in ModelSurrNonH)

int	pointsTotal
	user-specified lower bound on total points with which to build the model (if reuse_points < pointsTotal, new samples will make up the difference)

short	pointsManagement
	points management configuration for DataFitSurrModel: DEFAULT_POINTS, MINIMUM_POINTS, or RECOMMENDED_POINTS

String	approxPointReuse
	sample reuse selection for building global approximations: none, all, region, or file (from the `reuse_samples` specification in ModelSurrG)

String	importBuildPtsFile
	the file name from the `import_build_points_file` specification in ModelSurrG

unsigned short	importBuildFormat
	tabular format for the build point import file

bool	importUseVariableLabels
	whether to parse/validate variable labels from header

bool	importBuildActive
	whether to import active variables only

String	exportApproxPtsFile
	the file name from the `export_approx_points_file` specification in ModelSurrG

unsigned short	exportApproxFormat
	tabular format for the approx point export file

String	exportApproxVarianceFile
	filename for surrogate variance evaluation export

unsigned short	exportApproxVarianceFormat
	tabular format for the approx variance export file

bool	exportSurrogate
	Option to turn on surrogate model export (export_model)

String	modelExportPrefix
	the filename prefix for export_model

unsigned short	modelExportFormat
	Format selection for export_model.

bool	importSurrogate
	Option to turn on surrogate model import (import_model)

String	modelImportPrefix
	the filename prefix for import_model

unsigned short	modelImportFormat
	Format selection for import_model.

short	approxCorrectionType
	correction type for global and hierarchical approximations: NO_CORRECTION, ADDITIVE_CORRECTION, MULTIPLICATIVE_CORRECTION, or COMBINED_CORRECTION (from the `correction` specification in ModelSurrG and ModelSurrH)

short	approxCorrectionOrder
	correction order for global and hierarchical approximations: 0, 1, or 2 (from the `correction` specification in ModelSurrG and ModelSurrH)

bool	modelUseDerivsFlag
	flags the use of derivatives in building global approximations (from the `use_derivatives` specification in ModelSurrG)

bool	respScalingFlag
	flag to indicate bounds-based scaling of current response data set prior to surrogate build; important for data fits of decaying discrepancy data using regression with absolute tolerances

short	polynomialOrder
	scalar integer indicating the order of the polynomial approximation (1=linear, 2=quadratic, 3=cubic; from the `polynomial` specification in ModelSurrG)

RealVector	krigingCorrelations
	vector of correlations used in building a kriging approximation (from the `correlations` specification in ModelSurrG)

String	krigingOptMethod
	optimization method to use in finding optimal correlation parameters: none, sampling, local, global

short	krigingMaxTrials
	maximum number of trials in optimization of kriging correlations

RealVector	krigingMaxCorrelations
	upper bound on kriging correlation vector

RealVector	krigingMinCorrelations
	lower bound on kriging correlation vector

Real	krigingNugget
	nugget value for kriging

short	krigingFindNugget
	option to have Kriging find the best nugget value to use

short	mlsWeightFunction
	weight function for moving least squares approximation

short	rbfBases
	bases for radial basis function approximation

short	rbfMaxPts
	maximum number of points for radial basis function approximation

short	rbfMaxSubsets
	maximum number of subsets for radial basis function approximation

short	rbfMinPartition
	minimum partition for radial basis function approximation

short	marsMaxBases
	maximum number of bases for MARS approximation

String	marsInterpolation
	interpolation type for MARS approximation

short	annRandomWeight
	random weight for artificial neural network approximation

short	annNodes
	number of nodes for artificial neural network approximation

Real	annRange
	range for artificial neural network approximation

int	numRestarts
	number of restarts for gradient-based optimization in GP

bool	domainDecomp
	whether domain decomposition is enabled

String	decompCellType
	type of local cell of domain decomp

int	decompSupportLayers
	number of support layers for each local basis function

bool	decompDiscontDetect
	whether discontinuity detection is enabled

Real	discontJumpThresh
	function value (jump) threshold for discontinuity detection in domain decomp

Real	discontGradThresh
	gradient threshold for discontinuity detection in domain decomp

String	trendOrder
	scalar integer indicating the order of the Gaussian process mean (0= constant, 1=linear, 2=quadratic, 3=cubic); from the `gaussian_process` specification in ModelSurrG)

bool	pointSelection
	flag indicating the use of point selection in the Gaussian process

StringArray	diagMetrics
	List of diagnostic metrics the user requests to assess the goodness of fit for a surrogate model.

bool	crossValidateFlag
	flag indicating the use of cross validation on the metrics specified

int	numFolds
	number of folds to perform in cross validation

Real	percentFold
	percentage of data to withhold for cross validation process

bool	pressFlag
	flag indicating the use of PRESS on the metrics specified

String	importChallengePtsFile
	the file name from the `challenge_points_file` specification in ModelSurrG

unsigned short	importChallengeFormat
	tabular format of the challenge data file

bool	importChalUseVariableLabels
	whether to parse/validate variable labels from header

bool	importChallengeActive
	whether to import active variables only

String	advancedOptionsFilename
	file containing advanced surrogate option overrides

String	optionalInterfRespPointer
	string pointer to the responses specification used by the optional interface in nested models (from the `optional_interface_responses_pointer` specification in ModelNested)

StringArray	primaryVarMaps
	the primary variable mappings used in nested models for identifying the lower level variable targets for inserting top level variable values (from the `primary_variable_mapping` specification in ModelNested)

StringArray	secondaryVarMaps
	the secondary variable mappings used in nested models for identifying the (distribution) parameter targets within the lower level variables for inserting top level variable values (from the `secondary_variable_mapping` specification in ModelNested)

RealVector	primaryRespCoeffs
	the primary response mapping matrix used in nested models for weighting contributions from the sub-iterator responses in the top level (objective) functions (from the `primary_response_mapping` specification in ModelNested)

RealVector	secondaryRespCoeffs
	the secondary response mapping matrix used in nested models for weighting contributions from the sub-iterator responses in the top level (constraint) functions (from the `secondary_response_mapping` specification in ModelNested)

bool	identityRespMap
	whether an identity response map is requested in lieu of explicit maps

int	subMethodServers
	number of servers for concurrent sub-iterator parallelism

int	subMethodProcs
	number of processors for each concurrent sub-iterator partition

short	subMethodScheduling
	scheduling approach for concurrent sub-iterator parallelism: {DEFAULT,MASTER,PEER}_SCHEDULING

int	initialSamples
	initial samples to build the subspace model

unsigned short	subspaceSampleType
	sampling method for building the subspace model

IntVector	refineSamples
	refinement samples to add in each batch

size_t	maxIterations
	maximum number of subspace build iterations

Real	convergenceTolerance
	convergence tolerance on build process

bool	subspaceIdBingLi
	Flag to use Bing Li method to identify active subspace dimension.

bool	subspaceIdConstantine
	Flag to use Constantine method to identify active subspace dimension.

bool	subspaceIdEnergy
	Flag to use eigenvalue energy method to identify active subspace dimension.

bool	subspaceBuildSurrogate
	Flag to build surrogate over active subspace.

int	subspaceDimension
	Size of subspace.

unsigned short	subspaceNormalization
	Normalization to use when forming a subspace with multiple response functions.

int	numReplicates
	Number of bootstrap samples for subspace identification.

bool	subspaceIdCV
	Flag to use cross validation to identify active subspace dimension.

Real	relTolerance
	relative tolerance used by cross validation subspace dimension id method

Real	decreaseTolerance
	decrease tolerance used by cross validation subspace dimension id method

int	subspaceCVMaxRank
	maximum rank considered by cross validation subspace dimension id method

bool	subspaceCVIncremental
	flag to use incremental dimension estimation in the cross validation metric

unsigned short	subspaceIdCVMethod
	Contains which cutoff method to use in the cross validation metric.

short	regressionType
	type of (regularized) regression: FT_LS or FT_RLS2

Real	regressionL2Penalty
	penalty parameter for regularized regression (FT_RLS2)

size_t	maxSolverIterations
	max iterations for optimization solver used in FT regression

int	maxCrossIterations
	maximum number of cross iterations

Real	solverTol
	optimization tolerance for FT regression

Real	solverRoundingTol
	Rounding tolerance for FT regression.

Real	statsRoundingTol
	arithmetic (rounding) tolerance for FT sums and products

bool	tensorGridFlag
	sub-sample a tensor grid for generating regression data

unsigned short	startOrder
	starting polynomial order

unsigned short	kickOrder
	polynomial order increment when adapting

unsigned short	maxOrder
	maximum order of basis polynomials

bool	adaptOrder
	whether or not to adapt order by cross validation

size_t	startRank
	starting rank

size_t	kickRank
	rank increase increment

size_t	maxRank
	maximum rank

bool	adaptRank
	whether or not to adapt rank

size_t	maxCVRankCandidates
	maximum number of cross-validation candidates for adaptRank

unsigned short	maxCVOrderCandidates
	maximum number of cross-validation candidates for adaptOrder

short	c3AdvanceType
	quantity to increment (start rank, start order, max rank, max order, max rank + max order) for FT (uniform) p-refinement

size_t	collocationPoints
	number of data points used in FT construction by regression

Real	collocationRatio
	ratio of number of points to nuber of unknowns

bool	autoRefine
	whether automatic surrogate refinement is enabled

size_t	maxFunctionEvals
	maximum evals in refinement

String	refineCVMetric
	metric to use in cross-validation guided refinement

int	softConvergenceLimit
	max number of iterations in refinement without improvement

int	refineCVFolds
	number of cross-validation folds in guided refinement

unsigned short	adaptedBasisSparseGridLev
	sparse grid level for low-order PCE used to compute rotation matrix within adapted basis approach to dimension reduction

unsigned short	adaptedBasisExpOrder
	expansion order for low-order PCE used to compute rotation matrix within adapted basis approach to dimension reduction

Real	adaptedBasisCollocRatio
	collocation ratio for low-order PCE used to compute rotation matrix within adapted basis approach to dimension reduction

short	method_rotation

Real	adaptedBasisTruncationTolerance

unsigned short	randomFieldIdForm
	Contains which type of random field model.

unsigned short	analyticCovIdForm
	Contains which type of analytic covariance function.

Real	truncationTolerance
	truncation tolerance on build process: percent variance explained

String	propagationModelPointer
	pointer to the model through which to propagate the random field

String	rfDataFileName
	File from which to build the random field.

Private Member Functions
	DataModelRep ()
	constructor

void	write (std::ostream &s) const
	write a DataModelRep object to an std::ostream

void	read (MPIUnpackBuffer &s)
	read a DataModelRep object from a packed MPI buffer

void	write (MPIPackBuffer &s) const
	write a DataModelRep object to a packed MPI buffer

Friends
class	DataModel
	the handle class can access attributes of the body class directly

Detailed Description

Body class for model specification data.

The DataModelRep class is used to contain the data from a model keyword specification. Default values are managed in the DataModelRep constructor. Data is public to avoid maintaining set/get functions, but is still encapsulated within ProblemDescDB since ProblemDescDB::dataModelList is private.

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

DataModel.hpp
DataModel.cpp

Public Member Functions

Public Attributes

Private Member Functions

Friends

Detailed Description