Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning

15 December 2020

Papers citing "Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning"

8 / 8 papers shown

Title
Astral: training physics-informed neural networks with error majorants V. Fanaskov Tianchi Yu Alexander Rudikov Ivan V. Oseledets 33 1 0 04 Jun 2024
A Finite Expression Method for Solving High-Dimensional Committor Problems Zezheng Song M. Cameron Haizhao Yang 16 6 0 21 Jun 2023
Finite Expression Method for Solving High-Dimensional Partial Differential Equations Senwei Liang Haizhao Yang 31 18 0 21 Jun 2022
On the Representation of Solutions to Elliptic PDEs in Barron Spaces Ziang Chen Jianfeng Lu Yulong Lu 35 26 0 14 Jun 2021
A Priori Generalization Error Analysis of Two-Layer Neural Networks for Solving High Dimensional Schrödinger Eigenvalue Problems Jianfeng Lu Yulong Lu 34 29 0 04 May 2021
Bayesian neural networks for weak solution of PDEs with uncertainty quantification Xiaoxuan Zhang K. Garikipati AI4CE 46 11 0 13 Jan 2021
Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains Ziqi Liu Wei Cai Zhi-Qin John Xu AI4CE 223 157 0 22 Jul 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 125 508 0 11 Mar 2020