76 citations · 298 across the 12 of their papers we have counts for
12 papers
Gradient-free online learning of subgrid-scale dynamics with neural emulators
Hugo Frezat, Ronan Fablet, Guillaume Balarac +1
In this paper, we propose a generic algorithm to train machine learning-based subgrid parametrizations online, i.e., with loss functions, but for non-differ…
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…
A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps
Hugo Frezat, Julien Le Sommer, Ronan Fablet +2
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 sim…
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…
A finite-size correction model for two-fluid Large-Eddy Simulation of particle-laden boundary layer flow
Antoine Mathieu, Julien Chauchat, Cyrille Bonamy +2
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 boun…
Numerical study of the cryogenic cooling of amplifiers for high power lasers
Morgane Bellec, N. Luchier, Guillaume Balarac +2
The French collaborative Trio4CLF project aims to understand and control the cryogenic cooling of amplifiers for high power (~1 PetaWatt) and high repetition rate (1-10 Hertz) lase…