Poster
in
Workshop: The Synergy of Scientific and Machine Learning Modelling (SynS & ML) Workshop
Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation
Chris Pedersen · Laure Zanna · Joan Bruna · Pavel Perezhogin
Keywords: [ closure modelling ] [ parameterizations ] [ turbulence ] [ Machine Learning ]
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.