Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method

18 May 2022

Papers citing "Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method"

9 / 9 papers shown

Title
GAN-MDF: A Method for Multi-fidelity Data Fusion in Digital Twins Lixue Liu Chao Zhang Dacheng Tao AI4CE 45 3 0 24 Jun 2021
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks Xiaoli Chen Liu Yang Jinqiao Duan George Karniadakis 80 83 0 24 Aug 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 141 923 0 28 Jul 2020
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating a class of inverse problems for PDEs Siddhartha Mishra Roberto Molinaro PINN 94 267 0 29 Jun 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 243 793 0 13 Mar 2020
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 125 1,393 0 30 Sep 2017
DGM: A deep learning algorithm for solving partial differential equations Justin A. Sirignano K. Spiliopoulos AI4CE 97 2,067 0 24 Aug 2017
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He Xinming Zhang Shaoqing Ren Jian Sun VLM 355 18,661 0 06 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.1K 150,433 0 22 Dec 2014