Resampling with neural networks for stochastic parameterization in multiscale systems

DOI10.1016/J.PHYSD.2021.132894OpenAlexW3015022738MaRDI QIDQ2077663

Wouter Edeling, Daan Crommelin

Publication date: 21 February 2022

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.01457

zbMATH Keywords

machine learningstochastic parameterizationmultiscale dynamical systemsconditional resampling

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Numerical methods for initial value problems involving ordinary differential equations (65L05) Simulation of dynamical systems (37M05) Neural nets and related approaches to inference from stochastic processes (62M45)

Uses Software

• GitHub

Cites Work

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• Data-driven parameterization of the generalized Langevin equation
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