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LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex
  Geometry

LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry

3 February 2023
Jian Cheng Wong
P. Chiu
C. Ooi
M. Dao
Yew-Soon Ong
    AI4CE
    PINN
ArXivPDFHTML

Papers citing "LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry"

6 / 6 papers shown
Title
Spline-PINN: Approaching PDEs without Data using Fast, Physics-Informed
  Hermite-Spline CNNs
Spline-PINN: Approaching PDEs without Data using Fast, Physics-Informed Hermite-Spline CNNs
Nils Wandel
Michael Weinmann
Michael Neidlin
Reinhard Klein
AI4CE
50
58
0
15 Sep 2021
Efficient training of physics-informed neural networks via importance
  sampling
Efficient training of physics-informed neural networks via importance sampling
M. A. Nabian
R. J. Gladstone
Hadi Meidani
DiffM
PINN
69
220
0
26 Apr 2021
Parallel Physics-Informed Neural Networks via Domain Decomposition
Parallel Physics-Informed Neural Networks via Domain Decomposition
K. Shukla
Ameya Dilip Jagtap
George Karniadakis
PINN
98
272
0
20 Apr 2021
SPINN: Sparse, Physics-based, and partially Interpretable Neural
  Networks for PDEs
SPINN: Sparse, Physics-based, and partially Interpretable Neural Networks for PDEs
A. A. Ramabathiran
P. Ramachandran
PINN
AI4CE
27
75
0
25 Feb 2021
Physics-informed neural networks with hard constraints for inverse
  design
Physics-informed neural networks with hard constraints for inverse design
Lu Lu
R. Pestourie
Wenjie Yao
Zhicheng Wang
F. Verdugo
Steven G. Johnson
PINN
39
489
0
09 Feb 2021
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
437
0
18 Dec 2020
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