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1911.11380
Cited By
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows
26 November 2019
Mathis Bode
M. Gauding
Zeyu Lian
D. Denker
M. Davidovic
K. Kleinheinz
J. Jitsev
H. Pitsch
AI4CE
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Papers citing
"Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows"
8 / 8 papers shown
Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution
Qinglong Cao
Zhengqin Xu
Chao Ma
Xiaokang Yang
Yuntian Chen
130
0
0
29 Jan 2024
Origin-Destination Network Generation via Gravity-Guided GAN
Can Rong
Huandong Wang
Yong Li
189
10
0
06 Jun 2023
Physics-informed Deep Super-resolution for Spatiotemporal Data
Pu Ren
Chengping Rao
Yang Liu
Zihan Ma
Qi Wang
Jianxin Wang
Hao Sun
276
14
0
02 Aug 2022
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics
Rajat Arora
AI4CE
235
12
0
30 Jun 2022
Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticity
Rajat Arora
AI4CE
237
7
0
16 Dec 2021
A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processes
Niket Sharma
Y. A. Liu
172
116
0
02 Dec 2021
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions
IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021
D. Svendsen
M. Piles
Jordi Munoz-Marí
D. Luengo
Luca Martino
Gustau Camps-Valls
185
17
0
16 Apr 2021
Physics-Informed Neural Network Super Resolution for Advection-Diffusion Models
Chulin Wang
E. Bentivegna
Wang Zhou
L. Klein
Bruce Elmegreen
DiffM
202
36
0
04 Nov 2020
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