$extrastylesheet
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Dakota
Version 6.2
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Class for using global nongradient-based optimization approaches to calculate interval bounds for epistemic uncertainty quantification. More...
Public Member Functions | |
| NonDGlobalSingleInterval (ProblemDescDB &problem_db, Model &model) | |
| constructor | |
| ~NonDGlobalSingleInterval () | |
| destructor | |
Protected Member Functions | |
| void | initialize () |
| perform any required initialization | |
| void | post_process_cell_results (bool maximize) |
| post-process a cell minimization/maximization result | |
| void | get_best_sample (bool maximize, 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.
1.7.6.1