SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural Networks

10 July 2024

Papers citing "SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural Networks"

5 / 5 papers shown

Title
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing Salah A. Faroughi N. Pawar C. Fernandes Maziar Raissi Subasish Das N. Kalantari S. K. Mahjour PINN AI4CE 53 50 0 14 Nov 2022
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 107 230 0 26 Apr 2021
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation Wuxinlin Cheng Chenhui Deng Zhiqiang Zhao Yaohui Cai Zhiru Zhang Zhuo Feng AAML 41 14 0 07 Feb 2021
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 121 906 0 28 Jul 2020
Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning Stefan Elfwing E. Uchibe Kenji Doya 126 1,717 0 10 Feb 2017