Inheritance diagram for NonDGlobalReliability:
Public Member Functions | |
| NonDGlobalReliability (Model &model) | |
| constructor | |
| ~NonDGlobalReliability () | |
| destructor | |
| void | quantify_uncertainty () |
| approximations of the cumulative distribution function of response | |
| void | print_results (ostream &s) |
| 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 () |
| 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) |
| 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) |
| 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 |
| current iterate should be accepted (must reduce violation) | |
| bool | lastIterateAccepted |
| this controls update of parameters for augmented Lagrangian merit fn | |
Static Private Attributes | |
| static NonDGlobalReliability * | nondGlobRelInstance |
| functions in order to avoid the need for static data | |
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.
1.5.1