Class for global reliability methods within DAKOTA/UQ. More...
Public Member Functions | |
NonDGlobalReliability (Model &model) | |
constructor | |
~NonDGlobalReliability () | |
destructor | |
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 RealVector &c_variables) |
expected improvement function for the GP | |
Real | expected_feasibility (const RealVector &expected_values, const RealVector &c_variables) |
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 | |
Static Private Attributes | |
static NonDGlobalReliability * | nondGlobRelInstance |
pointer to the active object instance used within the static evaluator functions in order to avoid the need for static data |
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.