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

26 November 2019

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

5 / 5 papers shown

Title
Origin-Destination Network Generation via Gravity-Guided GAN Can Rong Huandong Wang Yong Li 22 5 0 06 Jun 2023
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics Rajat Arora AI4CE 35 10 0 30 Jun 2022
Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticity Rajat Arora AI4CE 24 7 0 16 Dec 2021
A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processes Niket Sharma Y. A. Liu 9 80 0 02 Dec 2021
Physics-Informed Neural Network Super Resolution for Advection-Diffusion Models Chulin Wang E. Bentivegna Wang Zhou L. Klein Bruce Elmegreen DiffM 16 32 0 04 Nov 2020