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PIAT: Physics Informed Adversarial Training for Solving Partial
  Differential Equations

PIAT: Physics Informed Adversarial Training for Solving Partial Differential Equations

14 July 2022
S. Shekarpaz
Mohammad Azizmalayeri
M. Rohban
ArXivPDFHTML

Papers citing "PIAT: Physics Informed Adversarial Training for Solving Partial Differential Equations"

5 / 5 papers shown
Title
A Data-Centric Approach for Improving Adversarial Training Through the
  Lens of Out-of-Distribution Detection
A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection
Mohammad Azizmalayeri
Arman Zarei
Alireza Isavand
M. T. Manzuri
M. Rohban
OODD
32
0
0
25 Jan 2023
Parallel Physics-Informed Neural Networks via Domain Decomposition
Parallel Physics-Informed Neural Networks via Domain Decomposition
K. Shukla
Ameya Dilip Jagtap
George Karniadakis
PINN
101
272
0
20 Apr 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
493
0
09 Feb 2021
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
172
756
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
117
508
0
11 Mar 2020
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