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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"

5 / 5 papers shown
Title
Physics-informed neural networks for inverse problems in supersonic
  flows
Physics-informed neural networks for inverse problems in supersonic flows
Ameya Dilip Jagtap
Zhiping Mao
Nikolaus Adams
George Karniadakis
PINN
43
218
0
23 Feb 2022
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention
  Mechanism
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
L. McClenny
U. Braga-Neto
PINN
72
458
0
07 Sep 2020
When and why PINNs fail to train: A neural tangent kernel perspective
When and why PINNs fail to train: A neural tangent kernel perspective
Sizhuang He
Xinling Yu
P. Perdikaris
130
909
0
28 Jul 2020
On the Spectral Bias of Neural Networks
On the Spectral Bias of Neural Networks
Nasim Rahaman
A. Baratin
Devansh Arpit
Felix Dräxler
Min Lin
Fred Hamprecht
Yoshua Bengio
Aaron Courville
141
1,438
0
22 Jun 2018
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
  ImageNet Classification
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
VLM
323
18,613
0
06 Feb 2015
1