61 citations · 133 across the 11 of their papers we have counts for
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physics.flu-dyn2022★ 61 cited
A posteriori learning for quasi-geostrophic turbulence parametrization
Hugo Frezat, Julien Le Sommer, Ronan Fablet +2
The use of machine learning to build subgrid parametrizations for climate models is receiving growing attention. State-of-the-art strategies address the problem as a supervised lea…
physics.flu-dyn2020★ 43 cited
Physical invariance in neural networks for subgrid-scale scalar flux modeling
Hugo Frezat, Guillaume Balarac, Julien Le Sommer +2
In this paper we present a new strategy to model the subgrid-scale scalar flux in a three-dimensional turbulent incompressible flow using physics-informed neural networks (NNs). Wh…