Class for using global nongradient-based optimization approaches to calculate interval bounds for epistemic uncertainty quantification. More...
Public Member Functions | |
NonDGlobalSingleInterval (Model &model) | |
constructor | |
~NonDGlobalSingleInterval () | |
destructor | |
Protected Member Functions | |
void | initialize () |
perform any required initialization | |
void | post_process_cell_results (bool minimize) |
post-process a cell minimization/maximization result | |
void | get_best_sample (bool find_max, bool eval_approx) |
determine truthFnStar and approxFnStar | |
Private Attributes | |
size_t | statCntr |
counter for finalStatistics |
Class for using global nongradient-based optimization approaches to calculate interval bounds for epistemic uncertainty quantification.
The NonDGlobalSingleInterval class supports global nongradient-based optimization apporaches to determining interval bounds for epistemic UQ. The interval bounds may be on the entire function in the case of pure interval analysis (e.g. intervals on input = intervals on output), or the intervals may be on statistics of an "inner loop" aleatory analysis such as intervals on means, variances, or percentile levels. The preliminary implementation will use a Gaussian process surrogate to determine interval bounds.