RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs

17 April 2025

Papers citing "RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs"

8 / 8 papers shown

Title
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks Chen-Chun Wu Min Zhu Qinyan Tan Yadhu Kartha Lu Lu 93 383 0 21 Jul 2022
Learning robust marking policies for adaptive mesh refinement A. Gillette B. Keith S. Petrides 61 11 0 13 Jul 2022
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 141 923 0 28 Jul 2020
A reinforcement learning approach to rare trajectory sampling Dominic C. Rose Jamie F. Mair J. P. Garrahan 58 51 0 26 May 2020
nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications G. Pang M. DÉlia M. Parks George Karniadakis PINN 52 155 0 08 Apr 2020
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 99 1,549 0 10 Jul 2019
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
A Bayesian Sampling Approach to Exploration in Reinforcement Learning J. Asmuth Lihong Li Michael L. Littman A. Nouri David Wingate BDL 103 189 0 09 May 2012