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Dakota
Version 6.2
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Class for using local gradient-based optimization approaches to calculate interval bounds for epistemic uncertainty quantification. More...
Public Member Functions | |
NonDLocalSingleInterval (ProblemDescDB &problem_db, Model &model) | |
constructor | |
~NonDLocalSingleInterval () | |
destructor | |
Protected Member Functions | |
void | initialize () |
perform any required initialization | |
void | post_process_cell_results (bool maximize) |
post-process a cell minimization/maximization result | |
Private Attributes | |
size_t | statCntr |
counter for finalStatistics |
Class for using local gradient-based optimization approaches to calculate interval bounds for epistemic uncertainty quantification.
The NonDLocalSingleInterval class supports local gradient-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.