Learning unbounded-domain spatiotemporal differential equations using adaptive spectral methods
Rapidly developing machine learning methods have stimulated research interest in computationally reconstructing differential equations (DEs) from observational data, providing insight into the underlying mechanistic models. In this paper, we propose a new neural-ODE-based method that spectrally expa...
Κύριοι συγγραφείς: | Xia, M, Li, X, Shen, Q, Chou, T |
---|---|
Μορφή: | Journal article |
Γλώσσα: | English |
Έκδοση: |
Springer
2024
|
Παρόμοια τεκμήρια
Παρόμοια τεκμήρια
-
Spectrally adapted physics-informed neural networks for solving unbounded domain problems
ανά: Mingtao Xia, κ.ά.
Έκδοση: (2023-01-01) -
Efficient hermite spectral-galerkin methods for nonlocal diffusion equations in unbounded domains
ανά: Li, Huiyuan, κ.ά.
Έκδοση: (2023) -
Efficient mapped spectral methods for unbounded and exterior domains
ανά: Batubara, Johan
Έκδοση: (2008) -
Fourier spectral method for the fractional-in-space coupled Whitham–Broer–Kaup equations on unbounded domain
ανά: Zhao Li-Fang, κ.ά.
Έκδοση: (2024-08-01) -
Rational spectral methods for PDEs involving fractional Laplacian in unbounded domains
ανά: Tang, Tao, κ.ά.
Έκδοση: (2020)