A posteriori learning for quasi-geostrophic turbulence parametrization

no code implementations • 8 Apr 2022 • Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat

State-of-the-art strategies address the problem as a supervised learning task and optimize algorithms that predict subgrid fluxes based on information from coarse resolution models.

A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps

1 code implementation • 12 Nov 2021 • Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat

Modeling the subgrid-scale dynamics of reduced models is a long standing open problem that finds application in ocean, atmosphere and climate predictions where direct numerical simulation (DNS) is impossible.

Physical invariance in neural networks for subgrid-scale scalar flux modeling

1 code implementation • 9 Oct 2020 • Hugo Frezat, Guillaume Balarac, Julien Le Sommer, Ronan Fablet, Redouane Lguensat

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).

A finite-size correction model for two-fluid Large-Eddy Simulation of particle-laden boundary layer flow

no code implementations • 20 Jul 2020 • Antoine Mathieu, Julien Chauchat, Cyrille Bonamy, Guillaume Balarac, Tian-Jian Hsu

In this paper the capabilities of the turbulence-resolving Eulerian-Eulerian two-phase flow model to predict the suspension of mono-dispersed finite-sized solid particles in a boundary layer flow are investigated.

Fluid Dynamics