期刊
SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION
卷 6, 期 2, 页码 429-456出版社
SIAM PUBLICATIONS
DOI: 10.1137/16M1110005
关键词
uncertainty quantification; bound-to-bound data collaboration; consistency; model validation; inverse problem; computer models
资金
- U.S. Department of Energy, National Nuclear Security Administration [DE-NA0002375]
Bound-to-bound data collaboration provides a natural framework for addressing both forward and inverse uncertainty quantification problems. In this approach, quantity of interest models are constrained by related experimental observations with interval uncertainty. A collection of such models and observations is termed a dataset and carves out a feasible region in the parameter space. If a dataset has a nonempty feasible set, it is said to be consistent. In real-world applications, it is often the case that collections of models and observations are inconsistent. Revealing the source of this inconsistency, i.e., identifying which models and/or observations are problematic, is essential before a dataset can be used for prediction. To address this issue, we introduce a constraint relaxation- based approach, termed the vector consistency measure, for investigating datasets with numerous sources of inconsistency. The benefits of this vector consistency measure over a previous method of consistency analysis are demonstrated in two realistic gas combustion examples.
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