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Title

Using Physics-Informed Super-Resolution Generative Adversarial Networks
for Subgrid Modeling in Turbulent Reactive Flows

Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows

26 November 2019

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Papers citing "Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows"

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Title
Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution Qinglong Cao Zhengqin Xu Chao Ma Xiaokang Yang Yuntian Chen 122 0 0 29 Jan 2024
Origin-Destination Network Generation via Gravity-Guided GAN Can Rong Huandong Wang Yong Li 176 9 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 256 14 0 02 Aug 2022
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics Rajat Arora AI4CE 229 12 0 30 Jun 2022
Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticity Rajat Arora AI4CE 230 7 0 16 Dec 2021
A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processes Niket Sharma Y. A. Liu 157 116 0 02 Dec 2021
Integrating Domain Knowledge in Data-driven Earth Observation with Process ConvolutionsIEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021 D. Svendsen M. Piles Jordi Munoz-Marí D. Luengo Luca Martino Gustau Camps-Valls 176 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 201 36 0 04 Nov 2020