Methodological and theoretical advances in Physics-Informed Machine Learning

Recently, physics-informed learning (PIML) approaches have been highly successful in many fields ranging from physics to life and environmental sciences. These methodologies enable the hybridization of physical knowledge of the field and real data in order to solve a variety of problems. This national conference aims to bring together the various communities working on physics-informed ML approaches. The program will revolve on four main themes:

• statistical aspects;

• inverse problems;

• neural operators;

• modeling & numerical analysis