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.

Type
Publication
ICML, Workshop on The Synergy of Scientific and Machine Learning Modeling, arXiv:2307.13144
Chris Pedersen
Chris Pedersen
AI Researcher Startup
Laure Zanna
Laure Zanna
Joseph B. Keller and Herbert B. Keller Professor in Applied Mathematics; Professor of Mathematics and Data Science

My research interests include Climate Dynamics, Physical Oceanography, Applied Math, and Data Science.