Respecting causality is all you need for training physics-informed neural networks

14 March 2022

Papers citing "Respecting causality is all you need for training physics-informed neural networks"

22 / 122 papers shown

Title
Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks Daniel Amini E. Haghighat R. Juanes PINN AI4CE 25 32 0 07 Sep 2022
Semi-analytic PINN methods for singularly perturbed boundary value problems G. Gie Youngjoon Hong Chang-Yeol Jung PINN 8 5 0 19 Aug 2022
A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Applications to COVID-19 Haoran Hu Connor Kennedy P. Kevrekidis Hongkun Zhang 11 10 0 01 Aug 2022
PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers Namgyu Kang Byeonghyeon Lee Youngjoon Hong S. Yun Eunbyung Park PINN AI4CE 22 13 0 26 Jul 2022
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 32 353 0 21 Jul 2022
Unsupervised Legendre-Galerkin Neural Network for Singularly Perturbed Partial Differential Equations Junho Choi N. Kim Youngjoon Hong AI4CE 24 0 0 21 Jul 2022
Adaptive Self-supervision Algorithms for Physics-informed Neural Networks Shashank Subramanian Robert M. Kirby Michael W. Mahoney A. Gholami 30 25 0 08 Jul 2022
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling Arka Daw Jie Bu Sizhuang He P. Perdikaris Anuj Karpatne AI4CE 21 46 0 05 Jul 2022
Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks R. Mojgani Maciej Balajewicz P. Hassanzadeh PINN 33 45 0 05 May 2022
RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks Wei Peng Weien Zhou Xiaoya Zhang Wenjuan Yao Zheliang Liu 25 15 0 02 May 2022
Competitive Physics Informed Networks Qi Zeng Yash Kothari Spencer H. Bryngelson F. Schafer PINN 19 20 0 23 Apr 2022
Improved Training of Physics-Informed Neural Networks with Model Ensembles Katsiaryna Haitsiukevich Alexander Ilin PINN 36 23 0 11 Apr 2022
On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks Franz M. Rohrhofer S. Posch C. Gößnitzer Bernhard C. Geiger PINN 38 17 0 25 Mar 2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next S. Cuomo Vincenzo Schiano Di Cola F. Giampaolo G. Rozza Maizar Raissi F. Piccialli PINN 26 1,180 0 14 Jan 2022
Improved architectures and training algorithms for deep operator networks Sizhuang He Hanwen Wang P. Perdikaris AI4CE 52 105 0 04 Oct 2021
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations Benjamin Moseley Andrew Markham T. Nissen‐Meyer PINN 48 210 0 16 Jul 2021
Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks Franz M. Rohrhofer S. Posch C. Gößnitzer Bernhard C. Geiger PINN 23 39 0 03 May 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 71 223 0 26 Apr 2021
Parallel Physics-Informed Neural Networks via Domain Decomposition K. Shukla Ameya Dilip Jagtap George Karniadakis PINN 101 274 0 20 Apr 2021
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 44 494 0 09 Feb 2021
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks Sizhuang He Hanwen Wang P. Perdikaris 131 439 0 18 Dec 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 362 11,700 0 09 Mar 2017