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Can Physics-Informed Neural Networks beat the Finite Element Method?

Can Physics-Informed Neural Networks beat the Finite Element Method?

8 February 2023
T. G. Grossmann
U. J. Komorowska
J. Latz
Carola-Bibiane Schönlieb
    PINN
    AI4CE
ArXivPDFHTML

Papers citing "Can Physics-Informed Neural Networks beat the Finite Element Method?"

11 / 11 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
64
1
0
14 Dec 2024
Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups
Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups
Zakhar Shumaylov
Peter Zaika
James Rowbottom
Ferdia Sherry
Melanie Weber
Carola-Bibiane Schönlieb
34
1
0
03 Oct 2024
Improved physics-informed neural network in mitigating gradient related
  failures
Improved physics-informed neural network in mitigating gradient related failures
Pancheng Niu
Yongming Chen
Jun Guo
Yuqian Zhou
Minfu Feng
Yanchao Shi
PINN
AI4CE
18
0
0
28 Jul 2024
Weak baselines and reporting biases lead to overoptimism in machine
  learning for fluid-related partial differential equations
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
N. McGreivy
Ammar Hakim
AI4CE
29
42
0
09 Jul 2024
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs
Pau Batlle
Yifan Chen
Bamdad Hosseini
H. Owhadi
Andrew M. Stuart
26
17
0
08 May 2023
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
Rui Zhang
Qi Meng
Rongchan Zhu
Yue Wang
Wenlei Shi
Shihua Zhang
Zhi-Ming Ma
Tie-Yan Liu
DiffM
AI4CE
44
4
0
10 Feb 2023
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
45
209
0
16 Jul 2021
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
O. Hennigh
S. Narasimhan
M. A. Nabian
Akshay Subramaniam
Kaustubh Tangsali
M. Rietmann
J. Ferrandis
Wonmin Byeon
Z. Fang
S. Choudhry
PINN
AI4CE
93
126
0
14 Dec 2020
Fourier Neural Operator for Parametric Partial Differential Equations
Fourier Neural Operator for Parametric Partial Differential Equations
Zong-Yi Li
Nikola B. Kovachki
Kamyar Azizzadenesheli
Burigede Liu
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
203
2,282
0
18 Oct 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and
  Inverse PDE Problems with Noisy Data
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data
Liu Yang
Xuhui Meng
George Karniadakis
PINN
177
758
0
13 Mar 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain
  Decomposition
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition
E. Kharazmi
Zhongqiang Zhang
George Karniadakis
125
508
0
11 Mar 2020
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