Élie Hachem, professor at CEMEF Sophia Antipolis (Mines Paris), will give a seminar on Tuesday May 30 at 11 a.m. entitled “Machine Learning and Anisotropic Finite Element Framework for Computational Fluid Dynamics” (the summary is below, as well as a biography). This will take place in room 211, corridor 55-65 (note this room is not in the ISCD premises!), but here is a Zoom link for those who are interested and who cannot be present:
Meeting ID: 870 8949 0283
For information, you can follow the program of the next ISCD seminars on this link.
In this presentation, we will share our expertise in numerical developments built on a ground-breaking parallel adaptive anisotropic mesh refinement for challenging multiphysics simulations. This is shown to be efficient for several applictions such as multiphase flows, fluid structure interaction and conjugate heat transfer simulations. It allows for example handling high viscosity ratio when dealing with complex fluid flows or with high temperature gradients when dealing with evaporation and boiling. Finally, highlights on our latest developments and applications of machine learning in broad areas of CFD such as prediction, control and optimization will be presented.
About the speaker
Elie Hachem is a professor of fluid mechanics and applied mathematics at Mines Paris - PSL and head of the Computational Fluid Mechanics (CFL) research group at CEMEF UMR CNRS since 2014. His research work focuses on the development of advanced numerical methods for fluid mechanics coupled with Machine Learning. They combine anisotropic mesh adaptation, immersion methods and finite element methods stabilized by the variational multiscale method for the design and analysis of complex systems for different topics: aerothermal, multiphase, aerodynamics, fluid-structure interaction, complex fluids. He has received numerous professional and academic awards including: the IBM Faculty Award (2015), the Atos Jospeh-Fourier Award (2019) for the best team in high performance computing, and the IACM Fellow Award (2020) in computational mechanics and recently an ERC Consolidator Grant.