Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning

DOI10.1063/5.0113632WikidataQ117220140 ScholiaQ117220140MaRDI QIDQ6572673

Nikolaos Evangelou, Sebastian Reich, Alexei G. Makeev, George A. Kevrekidis, Felix Dietrich, I. G. Kevrekidis, Tom S. Bertalan

Publication date: 16 July 2024

Published in: Chaos (Search for Journal in Brave)

Mathematics Subject Classification ID

Dynamical systems and ergodic theory (37-XX) Ordinary differential equations (34-XX)

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Related Items (4)

DynGMA: a robust approach for learning stochastic differential equations from data ⋮ Tipping points of evolving epidemiological networks: machine learning-assisted, data-driven effective modeling ⋮ RandONets: shallow networks with random projections for learning linear and nonlinear operators ⋮ Modified stochastic diffusion particle-tracking model (MSDPTM) incorporating energy cascade theory and eddy intermittency for suspended sediment transport in open channel flow

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