Path-space variational inference for non-equilibrium coarse-grained systems

DOI10.1016/j.jcp.2016.03.021zbMath1349.65030arXiv1508.00289OpenAlexW2298009901WikidataQ60149102 ScholiaQ60149102MaRDI QIDQ2375138

Evangelia Kalligiannaki, Markos A. Katsoulakis, Petr Plecháč, Vagelis A. Harmandaris

Publication date: 5 December 2016

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1508.00289

zbMATH Keywords

stochastic optimizationtime seriesLangevin dynamicsmachine learningnon-equilibriumvariational inferenceinformation metricscoarse graining

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Numerical solutions to stochastic differential and integral equations (65C30) Statistical aspects of information-theoretic topics (62B10) Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) (60J70)

Related Items (11)

GINNs: graph-informed neural networks for multiscale physics ⋮ Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems ⋮ Scalable information inequalities for uncertainty quantification ⋮ Bottom-Up Transient Time Models in Coarse-Graining Molecular Systems ⋮ A Micro-Macro Acceleration Method for the Monte Carlo Simulation of Stochastic Differential Equations ⋮ An estimator for the relative entropy rate of path measures for stochastic differential equations ⋮ Robust Information Divergences for Model-Form Uncertainty Arising from Sparse Data in Random PDE ⋮ Study of micro-macro acceleration schemes for linear slow-fast stochastic differential equations with additive noise ⋮ Data-driven, variational model reduction of high-dimensional reaction networks ⋮ Physics-Constrained, Data-Driven Discovery of Coarse-Grained Dynamics ⋮ Special issue: Big data and predictive computational modeling

Uses Software

• PMTK

Cites Work

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