Predictive coarse-graining

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• 作者：Markus Schö ; berl^a ; ^{m.schoeberl@tum.de} ; Nicholas Zabaras^b ; ^c ; ^{nzabaras@gmail.com} ; Phaedon-Stelios Koutsourelakis^a ; ^{p.s.koutsourelakis@tum.de}
• 关键词：Coarse-graining ; Generative models ; Bayesian ; Uncertainty quantification ; SPC/E water ; Lattice systems
• 刊名：Journal of Computational Physics
• 出版年：2017
• 出版时间：15 March 2017
• 年：2017
• 卷：333
• 期：Complete
• 页码：49-77
• 全文大小：1643 K
• 卷排序：333

文摘

We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics. In contrast to existing techniques which are based on a fine-to-coarse map, we adopt the opposite strategy by prescribing a probabilistic coarse-to-fine map. This corresponds to a directed probabilistic model where the coarse variables play the role of latent generators of the fine scale (all-atom) data. From an information-theoretic perspective, the framework proposed provides an improvement upon the relative entropy method [1] and is capable of quantifying the uncertainty due to the information loss that unavoidably takes place during the coarse-graining process. Furthermore, it can be readily extended to a fully Bayesian model where various sources of uncertainties are reflected in the posterior of the model parameters. The latter can be used to produce not only point estimates of fine-scale reconstructions or macroscopic observables, but more importantly, predictive posterior distributions on these quantities. Predictive posterior distributions reflect the confidence of the model as a function of the amount of data and the level of coarse-graining. The issues of model complexity and model selection are seamlessly addressed by employing a hierarchical prior that favors the discovery of sparse solutions, revealing the most prominent features in the coarse-grained model. A flexible and parallelizable Monte Carlo – Expectation–Maximization (MC-EM) scheme is proposed for carrying out inference and learning tasks. A comparative assessment of the proposed methodology is presented for a lattice spin system and the SPC/E water model.