Seminar by Jordan Frécon-Deloire

"Physics-Informed Machine Learning" by Jordan Frécon-Deloire

at 11:00 AM

Room F021b

Building F

Laboratoire Hubert Curien

18 rue du Professeur Benoît Lauras

42000 Saint-Etienne

Seminar by Jordan Frécon-Deloire


This presentation will elucidate the core principles of physics-informed machine learning. The first part will focus on physics-informed neural networks (PINNs), detailing the methodology of modeling and training neural networks to efficiently approximate solutions to partial differential equations (PDEs) by incorporating physical knowledge. The second section will address the development of surrogates for a family of PDE solutions where, for instance, the initial conditions or PDE coefficients vary. Within this context, I will explore how neural operators, originally devised for mapping between two infinite-dimensional functions, can be informed by the underlying physics.

This seminar will be held in English.