Deep learning nonlinear multiscale dynamic problems using Koopman operator
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Publication:2133546
DOI10.1016/j.jcp.2021.110660OpenAlexW3195789587MaRDI QIDQ2133546
Publication date: 29 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2021.110660
Basic methods in fluid mechanics (76Mxx) Numerical methods for partial differential equations, boundary value problems (65Nxx) Approximation methods and numerical treatment of dynamical systems (37Mxx)
Related Items (5)
Correcting noisy dynamic mode decomposition with Kalman filters ⋮ Data-driven probability density forecast for stochastic dynamical systems ⋮ Koopman operator learning using invertible neural networks ⋮ Deep neural network based adaptive learning for switched systems ⋮ Data-driven reduced-order modeling for nonautonomous dynamical systems in multiscale media
Uses Software
Cites Work
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