Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs

14 February 2024

Papers citing "Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs"

5 / 5 papers shown

Title
Pseudo-Hamiltonian neural networks for learning partial differential equations Sølve Eidnes K. Lye 16 10 0 27 Apr 2023
Generic bounds on the approximation error for physics-informed (and) operator learning Tim De Ryck Siddhartha Mishra PINN 59 59 0 23 May 2022
Multiwavelet-based Operator Learning for Differential Equations Gaurav Gupta Xiongye Xiao P. Bogdan 124 200 0 28 Sep 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 203 2,281 0 18 Oct 2020
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 139 219 0 29 Sep 2019