Robust information divergences for model-form uncertainty arising from sparse data in random PDE
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Publication:6290029
DOI10.1137/17M1143344arXiv1708.03718WikidataQ129161048 ScholiaQ129161048MaRDI QIDQ6290029
Markos A. Katsoulakis, Eric J. Hall
Publication date: 11 August 2017
Abstract: We develop a novel application of hybrid information divergences to analyze uncertainty in steady-state subsurface flow problems. These hybrid information divergences are non-intrusive, goal-oriented uncertainty quantification tools that enable robust, data-informed predictions in support of critical decision tasks such as regulatory assessment and risk management. We study the propagation of model-form or epistemic uncertainty with numerical experiments that demonstrate uncertainty quantification bounds for (i) parametric sensitivity analysis and (ii) model misspecification due to sparse data. Further, we make connections between the hybrid information divergences and certain concentration inequalities that can be leveraged for efficient computing and account for any available data through suitable statistical quantities.
Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Measures of information, entropy (94A17)
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