Stacked networks improve physics-informed training: applications to
neural networks and deep operator networks

Stacked networks improve physics-informed training: applications to neural networks and deep operator networks

11 November 2023

Amanda A. Howard

Sarah H. Murphy

Papers citing "Stacked networks improve physics-informed training: applications to neural networks and deep operator networks"

10 / 10 papers shown

Title
Deep Learning without Global Optimization by Random Fourier Neural Networks Owen Davis Gianluca Geraci Mohammad Motamed BDL 43 0 0 16 Jul 2024
Ensemble and Mixture-of-Experts DeepONets For Operator Learning Ramansh Sharma Varun Shankar 45 0 0 20 May 2024
Machine learning of hidden variables in multiscale fluid simulation A. Joglekar A. Thomas AI4CE 42 8 0 19 Jun 2023
A multifidelity approach to continual learning for physical systems Amanda A. Howard Yucheng Fu P. Stinis AI4CE CLL 31 8 0 08 Apr 2023
Applying Machine Learning to Study Fluid Mechanics Steven L. Brunton PINN AI4CE 29 94 0 05 Oct 2021
Improved architectures and training algorithms for deep operator networks Sifan Wang Hanwen Wang P. Perdikaris AI4CE 37 102 0 04 Oct 2021
DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks Christian Moya Guang Lin AI4CE PINN 40 37 0 09 Sep 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 35 206 0 16 Jul 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 55 218 0 26 Apr 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 197 2,254 0 18 Oct 2020