Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems
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Publication:2125437
DOI10.1016/j.jcp.2020.109673OpenAlexW3039213484MaRDI QIDQ2125437
Phaedon-Stelios Koutsourelakis, Sebastian Kaltenbach
Publication date: 14 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1912.12976
reduced order modelingmultiscale modelingcoarse grainingBayesian machine learningvirtual observables
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A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables, Numerical analysis of non-local calculus on finite weighted graphs, with application to reduced-order modeling of dynamical systems, Fully probabilistic deep models for forward and inverse problems in parametric PDEs, Semi-supervised invertible neural operators for Bayesian inverse problems
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