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arXiv · 1605.04184

Scalable Information Inequalities for Uncertainty Quantification

Markos A. Katsoulakis·Luc Rey-Bellet·Jie Wang
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Abstract

In this paper we demonstrate the only available scalable information bounds for quantities of interest of high dimensional probabilistic models. Scalability of inequalities allows us to (a) obtain uncertainty quantification bounds for quantities of interest in the large degree of freedom limit and/or at long time regimes; (b) assess the impact of large model perturbations as in nonlinear response regimes in statistical mechanics; (c) address model-form uncertainty, i.e. compare different extended models and corresponding quantities of interest. We demonstrate some of these properties by deriving robust uncertainty quantification bounds for phase diagrams in statistical mechanics models.

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Markos A. Katsoulakis, Luc Rey-Bellet, Jie Wang. 2016-05-13. Scalable Information Inequalities for Uncertainty Quantification. https://doi.org/10.1016/j.jcp.2017.02.020

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