Galerkin Neural Networks: A Framework for Approximating Variational Equations with Error Control

28 May 2021

Papers citing "Galerkin Neural Networks: A Framework for Approximating Variational Equations with Error Control"

9 / 9 papers shown

Title
Deep Learning without Global Optimization by Random Fourier Neural Networks Owen Davis Gianluca Geraci Mohammad Motamed BDL 96 0 0 16 Jul 2024
Neural Networks Trained by Weight Permutation are Universal Approximators Yongqiang Cai Gaohang Chen Zhonghua Qiao 165 1 0 01 Jul 2024
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs Siddhartha Mishra Roberto Molinaro PINN 65 173 0 29 Jun 2020
Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint E. Cyr Mamikon A. Gulian Ravi G. Patel M. Perego N. Trask 83 72 0 10 Dec 2019
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations E. Kharazmi Z. Zhang George Karniadakis 74 244 0 27 Nov 2019
A unified deep artificial neural network approach to partial differential equations in complex geometries Jens Berg K. Nystrom AI4CE 61 586 0 17 Nov 2017
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 121 1,387 0 30 Sep 2017
DGM: A deep learning algorithm for solving partial differential equations Justin A. Sirignano K. Spiliopoulos AI4CE 91 2,063 0 24 Aug 2017
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.8K 150,115 0 22 Dec 2014