# Generative Modeling of Turbulence

@article{Drygala2022GenerativeMO,
title={Generative Modeling of Turbulence},
author={Claudia Drygala and Benjamin Winhart and Francesca di Mare and Hanno Gottschalk},
journal={ArXiv},
year={2022},
volume={abs/2112.02548}
}
• Published 5 December 2021
• Computer Science
• ArXiv
We present a mathematically well-founded approach for the synthetic modeling of turbulent flows using generative adversarial networks (GAN). Based on the analysis of chaotic, deterministic systems in terms of ergodicity, we outline a mathematical proof that GAN can actually learn to sample state snapshots from the invariant measure of the chaotic system. Based on this analysis, we study a hierarchy of chaotic systems starting with the Lorenz attractor and then carry on to the modeling of…
2 Citations

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