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1910.13444
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Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
29 October 2019
Liu Yang
Sean Treichler
Thorsten Kurth
Keno Fischer
D. Barajas-Solano
Josh Romero
Valentin Churavy
A. Tartakovsky
Michael Houston
P. Prabhat
George Karniadakis
AI4CE
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Papers citing
"Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs"
7 / 7 papers shown
Title
Generative Adversarial Reduced Order Modelling
Dario Coscia
N. Demo
G. Rozza
GAN
AI4CE
34
5
0
25 May 2023
Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
Xuan Di
Rongye Shi
Zhaobin Mo
Yongjie Fu
PINN
AI4TS
AI4CE
26
28
0
03 Mar 2023
PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations
Weiheng Zhong
Hadi Meidani
DRL
19
36
0
21 Mar 2022
Reinventing High Performance Computing: Challenges and Opportunities
Daniel Reed
Dennis Gannon
Jack J. Dongarra
AILaw
14
30
0
04 Mar 2022
PCNN: A physics-constrained neural network for multiphase flows
Haoyang Zheng
Ziyang Huang
Guang Lin
PINN
19
8
0
18 Sep 2021
Distributed Multigrid Neural Solvers on Megavoxel Domains
Aditya Balu
Sergio Botelho
Biswajit Khara
Vinay Rao
C. Hegde
S. Sarkar
Santi S. Adavani
A. Krishnamurthy
Baskar Ganapathysubramanian
AI4CE
6
11
0
29 Apr 2021
A Style-Based Generator Architecture for Generative Adversarial Networks
Tero Karras
S. Laine
Timo Aila
279
10,354
0
12 Dec 2018
1