Title : Physics-informed Machine Learning for Computational Fluid Dynamics
Début de thèse : 09/12/2024
Fin de thèse :08/12/2027
Abstract: To investigate the possibility of integrating learning models as part of the CFD workflow. Traditional numerical solvers come with a high computaional cost. The goal is to design surrogate models to approximate solutions to PDEs faster. To facilitate this problem, we employ, physics-informed machine learning models which have both a data-driven component, as well as, a PDE component to adhere to solutions that make sense in the framework of the physical system.
Our work is in the context of, but not limited to, approximating flow fields around automotive vehicles. This is particularly challenging because we aim to account for the effect of changing, parametrized shape. More formally, it translates to, solving boundary-value-problems with a changing boundary.
Keywords: Machine learning, partial differential equations, physics-informed learning, surrogate modelling, computational fluid dynamics
Date de soutenance prévue : —
Encadrement :
- Directeur de thèse : Didier Rulliere, Mines Saint-Étienne
Partenaires ou/et Financeurs :
Stellantis
Objectifs de développement durable concernés :
