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Deep learning of free boundary and Stefan problems

Deep learning of free boundary and Stefan problems

4 June 2020
Sizhuang He
P. Perdikaris
ArXivPDFHTML

Papers citing "Deep learning of free boundary and Stefan problems"

13 / 13 papers shown
Title
ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning
ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning
Nilo Schwencke
Cyril Furtlehner
66
1
0
14 Dec 2024
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective
Sunwoong Yang
Hojin Kim
Y. Hong
K. Yee
R. Maulik
Namwoo Kang
PINN
AI4CE
23
17
0
05 Jan 2024
Multiphysics discovery with moving boundaries using Ensemble SINDy and
  Peridynamic Differential Operator
Multiphysics discovery with moving boundaries using Ensemble SINDy and Peridynamic Differential Operator
A. Bekar
E. Haghighat
E. Madenci
AI4CE
22
2
0
27 Mar 2023
Deep neural network expressivity for optimal stopping problems
Deep neural network expressivity for optimal stopping problems
Lukas Gonon
11
6
0
19 Oct 2022
Respecting causality is all you need for training physics-informed
  neural networks
Respecting causality is all you need for training physics-informed neural networks
Sizhuang He
Shyam Sankaran
P. Perdikaris
PINN
CML
AI4CE
40
199
0
14 Mar 2022
Scientific Machine Learning through Physics-Informed Neural Networks:
  Where we are and What's next
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
S. Cuomo
Vincenzo Schiano Di Cola
F. Giampaolo
G. Rozza
Maizar Raissi
F. Piccialli
PINN
26
1,179
0
14 Jan 2022
One-Shot Transfer Learning of Physics-Informed Neural Networks
One-Shot Transfer Learning of Physics-Informed Neural Networks
Shaan Desai
M. Mattheakis
H. Joy
P. Protopapas
Stephen J. Roberts
PINN
AI4CE
27
58
0
21 Oct 2021
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
49
105
0
04 Oct 2021
Applying physics-based loss functions to neural networks for improved
  generalizability in mechanics problems
Applying physics-based loss functions to neural networks for improved generalizability in mechanics problems
Samuel J. Raymond
David B. Camarillo
PINN
AI4CE
36
12
0
30 Apr 2021
Bayesian neural networks for weak solution of PDEs with uncertainty
  quantification
Bayesian neural networks for weak solution of PDEs with uncertainty quantification
Xiaoxuan Zhang
K. Garikipati
AI4CE
46
11
0
13 Jan 2021
An overview on deep learning-based approximation methods for partial
  differential equations
An overview on deep learning-based approximation methods for partial differential equations
C. Beck
Martin Hutzenthaler
Arnulf Jentzen
Benno Kuckuck
30
146
0
22 Dec 2020
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
Sizhuang He
Hanwen Wang
P. Perdikaris
131
438
0
18 Dec 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
33
877
0
28 Jul 2020
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