EUR SLEIGHT PIMALEA
(2023-2024)

Partners:

• JP Colombier (co-coordinator)/ LabHC (UJM, CNRS)

“During the past few years, machine learning has beenmassively used to better understand multiscale physics, e.g. (i) by overcoming the limitations of costly numerical solvers of Partial Differential Equations (PDE), (ii) by learning from data the residuals of known physical models or more recently (iii) by discovering the latter explaining the underlying dynamics of observed data. In this context, a new generation of machine learning models emerged by integrating physical information to guide the learning process. This collection of techniques is generally grouped in under the “Physics-guided” or “Physics-informed” machine learning topic. In order to train the corresponding deep neural networks, these methods typically assume that they have access to a large enough amount of empirical data and/or they know the underlying physics allowing the generation of simulated examples. The ambitious goal of this project PIMALEA is to achieve scientific breakthroughs in the domain of self-organization of matter whose specificities constitute important pitfalls for a direct use ofmachine learning.”