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2311.06483
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Stacked networks improve physics-informed training: applications to neural networks and deep operator networks
11 November 2023
Amanda A. Howard
Sarah H. Murphy
Shady E. Ahmed
P. Stinis
AI4CE
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Papers citing
"Stacked networks improve physics-informed training: applications to neural networks and deep operator networks"
11 / 11 papers shown
Title
Deep Learning without Global Optimization by Random Fourier Neural Networks
Owen Davis
Gianluca Geraci
Mohammad Motamed
BDL
60
0
0
16 Jul 2024
Ensemble and Mixture-of-Experts DeepONets For Operator Learning
Ramansh Sharma
Varun Shankar
60
0
0
20 May 2024
Machine learning and domain decomposition methods -- a survey
A. Klawonn
M. Lanser
J. Weber
AI4CE
24
7
0
21 Dec 2023
Machine learning of hidden variables in multiscale fluid simulation
A. Joglekar
A. Thomas
AI4CE
91
8
0
19 Jun 2023
A multifidelity approach to continual learning for physical systems
Amanda A. Howard
Yucheng Fu
P. Stinis
AI4CE
CLL
42
8
0
08 Apr 2023
Applying Machine Learning to Study Fluid Mechanics
Steven L. Brunton
PINN
AI4CE
39
95
0
05 Oct 2021
Improved architectures and training algorithms for deep operator networks
Sizhuang He
Hanwen Wang
P. Perdikaris
AI4CE
49
105
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
56
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
45
209
0
16 Jul 2021
Efficient training of physics-informed neural networks via importance sampling
M. A. Nabian
R. J. Gladstone
Hadi Meidani
DiffM
PINN
71
222
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
220
2,287
0
18 Oct 2020
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