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Dakota  Version 6.2
Public Member Functions | Private Member Functions | Static Private Member Functions | Private Attributes | Static Private Attributes
NonDGlobalReliability Class Reference

Class for global reliability methods within DAKOTA/UQ. More...

Inheritance diagram for NonDGlobalReliability:
NonDReliability NonD Analyzer Iterator

List of all members.

Public Member Functions

 NonDGlobalReliability (ProblemDescDB &problem_db, Model &model)
 constructor
 ~NonDGlobalReliability ()
 destructor
void derived_init_communicators (ParLevLIter pl_iter)
 derived class contributions to initializing the communicators associated with this Iterator instance
void derived_set_communicators (ParLevLIter pl_iter)
 derived class contributions to setting the communicators associated with this Iterator instance
void derived_free_communicators (ParLevLIter pl_iter)
 derived class contributions to freeing the communicators associated with this Iterator instance
void quantify_uncertainty ()
 performs an uncertainty propagation using analytical reliability methods which solve constrained optimization problems to obtain approximations of the cumulative distribution function of response
void print_results (std::ostream &s)
 print the approximate mean, standard deviation, and importance factors when using the mean value method or the CDF/CCDF information when using MPP-search-based reliability methods

Private Member Functions

void optimize_gaussian_process ()
 construct the GP using EGO/SKO
void importance_sampling ()
 perform multimodal adaptive importance sampling on the GP
void get_best_sample ()
 determine current best solution from among sample data for expected imporovement function in Performance Measure Approach (PMA)
Real constraint_penalty (const Real &constraint, const RealVector &c_variables)
 calculate the penalty to be applied to the PMA constraint value
Real expected_improvement (const RealVector &expected_values, const Variables &recast_vars)
 expected improvement function for the GP
Real expected_feasibility (const RealVector &expected_values, const Variables &recast_vars)
 expected feasibility function for the GP

Static Private Member Functions

static void EIF_objective_eval (const Variables &sub_model_vars, const Variables &recast_vars, const Response &sub_model_response, Response &recast_response)
 static function used as the objective function in the Expected Improvement (EIF) problem formulation for PMA
static void EFF_objective_eval (const Variables &sub_model_vars, const Variables &recast_vars, const Response &sub_model_response, Response &recast_response)
 static function used as the objective function in the Expected Feasibility (EFF) problem formulation for RIA

Private Attributes

Real fnStar
 minimum penalized response from among true function evaluations
short meritFunctionType
 type of merit function used to penalize sample data
Real lagrangeMult
 Lagrange multiplier for standard Lagrangian merit function.
Real augLagrangeMult
 Lagrange multiplier for augmented Lagrangian merit function.
Real penaltyParameter
 penalty parameter for augmented Lagrangian merit funciton
Real lastConstraintViolation
 constraint violation at last iteration, used to determine if the current iterate should be accepted (must reduce violation)
bool lastIterateAccepted
 flag to determine if last iterate was accepted this controls update of parameters for augmented Lagrangian merit fn
short dataOrder
 order of the data used for surrogate construction, in ActiveSet request vector 3-bit format; user may override responses spec

Static Private Attributes

static NonDGlobalReliabilitynondGlobRelInstance
 pointer to the active object instance used within the static evaluator functions in order to avoid the need for static data

Detailed Description

Class for global reliability methods within DAKOTA/UQ.

The NonDGlobalReliability class implements EGO/SKO for global MPP search, which maximizes an expected improvement function derived from Gaussian process models. Once the limit state has been characterized, a multimodal importance sampling approach is used to compute probabilities.


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