Data-driven reduced-order modeling of spatiotemporal chaos with neural ordinary differential equations

DOI10.1063/5.0069536MaRDI QIDQ6565124

[[Person:6080312|Author name not available (Why is that?)]], Michael D. Graham

Publication date: 1 July 2024

Published in: (Search for Journal in Brave)

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Infinite-dimensional dissipative dynamical systems (37Lxx) Qualitative properties of solutions to partial differential equations (35Bxx)

Cites Work

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Related Items (4)

Data-driven modeling and forecasting of chaotic dynamics on inertial manifolds constructed as spectral submanifolds ⋮ Enhancing predictive capabilities in data-driven dynamical modeling with automatic differentiation: Koopman and neural ODE approaches ⋮ Divide and conquer: learning chaotic dynamical systems with multistep penalty neural ordinary differential equations ⋮ Neural dynamical operator: continuous spatial-temporal model with gradient-based and derivative-free optimization methods

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