wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws

18 July 2022

Papers citing "wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws"

8 / 8 papers shown

Title
A Deep Learning Framework for Solving Hyperbolic Partial Differential Equations: Part I Rajat Arora PINN AI4CE 27 1 0 09 Jul 2023
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities S. Lanthaler Roberto Molinaro Patrik Hadorn Siddhartha Mishra 56 24 0 03 Oct 2022
Variationally Mimetic Operator Networks Dhruv V. Patel Deep Ray M. Abdelmalik T. Hughes Assad A. Oberai 55 23 0 26 Sep 2022
Error analysis for deep neural network approximations of parametric hyperbolic conservation laws Tim De Ryck Siddhartha Mishra PINN 15 10 0 15 Jul 2022
Generic bounds on the approximation error for physics-informed (and) operator learning Tim De Ryck Siddhartha Mishra PINN 61 59 0 23 May 2022
Parallel Physics-Informed Neural Networks via Domain Decomposition K. Shukla Ameya Dilip Jagtap George Karniadakis PINN 101 274 0 20 Apr 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 238 2,298 0 18 Oct 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 183 760 0 13 Mar 2020