We propose a novel denoising diffusion generative model for predicting nonlinear fluid fields
named FluidDiff. By performing a diffusion process, the model is able to learn a complex …

Classification algorithms have recently found applications in computational physics for the
selection of numerical methods or models adapted to the environment and the state of the …

We are interested in the computational study of shock hydrodynamics, ie problems involving
compressible solids, liquids, and gases that undergo large deformation. These problems are …

An alternative approach to simulate dynamic, recurrent flows with very large time steps is
presented. Data-driven forecasts based on the method of analogs are made employing a set …

In this paper we propose a robust learning pipeline for inference in computational fluid
dynamics (CFD) systems in the presence of faulty sensor data. The standard methods for …

Uncertainty quantification in computational physics requires running many simulations. For
some industrial applications, the complexity of the numerical model is incompatible with the …

Tout d'abord, je souhaite remercier mon directeur de these David Ryckelynck et mes
encadrants Fabien Casenave et Nissrine Akkari pour l'opportunité qu'ils m'ont offerte de …