Data-Driven Learning for the Mori--Zwanzig Formalism: A Generalization of the Koopman Learning Framework

DOI10.1137/21M1401759zbMath1489.37096arXiv2101.05873OpenAlexW3125149060WikidataQ114978686 ScholiaQ114978686MaRDI QIDQ5023533

Yen Ting Lin, Yifeng Tian, Marian Anghel, Daniel Livescu

Publication date: 24 January 2022

Published in: SIAM Journal on Applied Dynamical Systems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2101.05873

zbMATH Keywords

memory effectsdynamic mode decompositionextended dynamic mode decompositiondata-drivengeneralized Langevin equationsMori-Zwanzig formalismapproximate Koopman learninggeneralized fluctuation-dissipation relationshipreduced-order dynamical system

Mathematics Subject Classification ID

Stochastic methods (Fokker-Planck, Langevin, etc.) applied to problems in time-dependent statistical mechanics (82C31) Approximation methods and numerical treatment of dynamical systems (37M99) Numerical problems in dynamical systems (65P99)

Related Items (2)

Regression-Based Projection for Learning Mori–Zwanzig Operators ⋮ Extension of dynamic mode decomposition for dynamic systems with incomplete information based on t-model of optimal prediction

Uses Software

• HODMD

Cites Work

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