Consistency Analysis for Massively Inconsistent Datasets in Bound-to-Bound Data Collaboration
| Autor: | Hegde, Arun, Li, Wenyu, Oreluk, James, Packard, Andrew, Frenklach, Michael |
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| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | SIAM/ASA J. Uncertainty Quantification, 6(2), 2018, pp. 429-456 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1137/16M1110005 |
| Popis: | Bound-to-Bound Data Collaboration (B2BDC) provides a natural framework for addressing both forward and inverse uncertainty quantification problems. In this approach, QOI (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 experiments 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, entitled 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. Comment: 31 pages, published in SIAM/ASA Journal on Uncertainty Quantification |
| Databáze: | arXiv |
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