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Multifidelity domain decomposition-based physics-informed neural
  networks and operators for time-dependent problems

Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems

15 January 2024
Alexander Heinlein
Amanda A. Howard
Damien Beecroft
P. Stinis
    AI4CE
ArXivPDFHTML

Papers citing "Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems"

3 / 3 papers shown
Title
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
Amanda A. Howard
Sarah H. Murphy
Shady E. Ahmed
P. Stinis
AI4CE
58
18
0
11 Nov 2023
Improved architectures and training algorithms for deep operator
  networks
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
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
1