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Taper-based scattering formulation of the Helmholtz equation to improve
  the training process of Physics-Informed Neural Networks

Taper-based scattering formulation of the Helmholtz equation to improve the training process of Physics-Informed Neural Networks

15 April 2024
W. Dörfler
Mehdi Elasmi
Tim Laufer
ArXivPDFHTML

Papers citing "Taper-based scattering formulation of the Helmholtz equation to improve the training process of Physics-Informed Neural Networks"

2 / 2 papers shown
Title
Hyper-parameter tuning of physics-informed neural networks: Application
  to Helmholtz problems
Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems
Paul Escapil-Inchauspé
G. A. Ruz
24
32
0
13 May 2022
On the eigenvector bias of Fourier feature networks: From regression to
  solving multi-scale PDEs with physics-informed neural networks
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
Sifan Wang
Hanwen Wang
P. Perdikaris
131
438
0
18 Dec 2020
1