Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation
Abstract
Integration of machine learning (ML) models of unresolved dynamics into numerical simulations of fluid dynamics has been demonstrated to improve the accuracy of coarse resolution simulations. However, when trained in a purely offline mode, integrating ML models into the numerical scheme can lead to instabilities. In the context of a 2D, quasi-geostrophic turbulent system, we demonstrate that including an additional network in the loss function, which emulates the state of the system into the future, produces offline-trained ML models that capture important subgrid processes, with improved stability properties.
Keywords
Cite
@article{arxiv.2307.13144,
title = {Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation},
author = {Christian Pedersen and Laure Zanna and Joan Bruna and Pavel Perezhogin},
journal= {arXiv preprint arXiv:2307.13144},
year = {2023}
}
Comments
Accepted after peer-review at the 1st workshop on Synergy of Scientific and Machine Learning Modeling, SynS & ML ICML, Honolulu, Hawaii, USA. July, 2023