Scalable information inequalities for uncertainty quantification

JOURNAL OF COMPUTATIONAL PHYSICS

卷:336

Article

Katsoulakis, Markos A.¹  Rey-Bellet, Luc¹  Wang, Jie¹ 

[1] Univ Massachusetts, Dept Math & Stat, Amherst, MA 01003 USA

关键词: Kullback Leibler divergence;    Information metrics;    Uncertainty quantification;    Statistical mechanics;    High dimensional systems;    Nonlinear response;    Phase diagrams;   

DOI  :  10.1016/j.jcp.2017.02.020

来源: Elsevier

【 摘 要 】

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. (C) 2017 Elsevier Inc. All rights reserved.

【 授权许可】

Free