Moving Sampling Physics-informed Neural Networks induced by Moving Mesh
PDE

v1v2v3v4 (latest)

Moving Sampling Physics-informed Neural Networks induced by Moving Mesh PDE

14 November 2023

ArXiv (abs)PDF HTML

Papers citing "Moving Sampling Physics-informed Neural Networks induced by Moving Mesh PDE"

9 / 9 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 91 383 0 21 Jul 2022
DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations Keju Tang Xiaoliang Wan Chao Yang 47 115 0 28 Dec 2021
Augmented KRnet for density estimation and approximation Xiaoliang Wan Keju Tang 44 5 0 26 May 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 125 235 0 26 Apr 2021
VAE-KRnet and its applications to variational Bayes Xiaoliang Wan Shuangqing Wei BDL DRL 67 13 0 29 Jun 2020
On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs Yeonjong Shin Jérome Darbon George Karniadakis PINN 67 79 0 03 Apr 2020
Frequency Bias in Neural Networks for Input of Non-Uniform Density Ronen Basri Meirav Galun Amnon Geifman David Jacobs Yoni Kasten S. Kritchman 92 185 0 10 Mar 2020
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 99 1,544 0 10 Jul 2019
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 125 1,390 0 30 Sep 2017