The influence of adversarial training on turbulence closure modeling

L. Nista*, C. Schumann, G. Scialabba, T. Grenga, H. Pitsch, A. Attili

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Over the last years, fundamental advancements in deep learning frameworks combined with the availability of large highly-resolved datasets, as well as the exponential improvement in computer hardware performance have shown great promise to move beyond classical equation-based models for the turbulence closure. Deep convolutional neural networks (CNN) can be used to super-resolve low-resolution simulations, thus they become attractive for large eddy simulation subfilter-scale modeling. However, these models often lack generalization capabilities and cannot guarantee fields with high-wavenumber details. To tackle those problems, the use of generative adversarial networks (GAN), which are composed of two competing neural networks (a generator and a discriminator) has been proposed. Despite the remarkable performance of GAN in single-image super-reconstruction, its application in turbulence modeling applications is relatively unexplored. In this work, the contribution of adversarial training is assessed by comparing two types of deep neural networks: a supervised CNN-type model and a semi-supervised GAN-based model. This study demonstrates the ability of the GAN architecture to produce high-quality super-reconstructed fields compared to standard deep convolutional networks, enhancing subgrid physical structures. The prolonged adversarial training leads to extracting underlying small-dimensional features in a semi-supervised manner and, consequently, improved turbulence statistics. Finally, it is shown that the propensity of the GAN training to run into convergence oscillations can be limited by a proper selection of the learning rate for both generator and discriminator.

Original languageEnglish
Title of host publicationAIAA SciTech Forum 2022
PublisherAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
ISBN (Print)9781624106316
Publication statusPublished - 29 Dec 2021
EventAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022 - San Diego, United States
Duration: 3 Jan 20227 Jan 2022

Publication series

NameAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022


ConferenceAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022
Country/TerritoryUnited States
CitySan Diego


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