Machine Learning, Computational Physics, and Numerical Studies
Contacts for this research theme
Patrice Abry, Benjamin Audit, Pierre Borgnat, Louis-Alexandre Couston, Corentin Herbert, Adrien Meynard, Pablo Jensen, Nelly Pustelnik, Stéphane Roux, Stéphane Santucci, Julian Tachella
Domain-Informed Machine Learning
Informing ML with models or prior knowledge can combine both traditional methods and deep learning ones for enhanced performance, efficiency, and stability, in an approach of Science for AI
Computational Physics and Machine Learning
Use of ML and DL to improve methods of computational physics, in an approach of AI for Science
Impact of digital technologies
Environmental impact and its mitigation of computation. Reflexion on the indirect impacts and the widespread use of these technologies.