A Multi-Pass GAN for Fluid Flow Super-Resolution (SCA 2019)

Authors:Maximilian Werhahn, You Xie, Mengyu Chu, Nils Thuerey; Technical University of Munich. Details at: Abstract: We propose a novel method to up-sample volumetric functions with generative neural networks using several orthogonal passes. Our method decomposes generative problems on Cartesian field functions into multiple smaller sub-problems that can be learned more efficiently. Specifically, we utilize two separate generative adversarial networks: