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.
Cite
Text
Pedersen et al. "Reliable Coarse-Grained Turbulent Simulations Through Combined Offline Learning and Neural Emulation." ICML 2023 Workshops: SynS_and_ML, 2023.Markdown
[Pedersen et al. "Reliable Coarse-Grained Turbulent Simulations Through Combined Offline Learning and Neural Emulation." ICML 2023 Workshops: SynS_and_ML, 2023.](https://mlanthology.org/icmlw/2023/pedersen2023icmlw-reliable/)BibTeX
@inproceedings{pedersen2023icmlw-reliable,
title = {{Reliable Coarse-Grained Turbulent Simulations Through Combined Offline Learning and Neural Emulation}},
author = {Pedersen, Christian and Zanna, Laure and Bruna, Joan and Perezhogin, Pavel},
booktitle = {ICML 2023 Workshops: SynS_and_ML},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/pedersen2023icmlw-reliable/}
}