Machine Learning Multiscale Processes

Nikita Kazeev, Eleonore Vissol-Gaudin, Mengyi Chen, Isabelle Guyon, Andrey E Ustyuzhanin

Keywords: multiscale, complex systems, manifold learning, physics-informed machine learning, physics, chemistry, materials, hydrodynamics, biology, cosmology, LLM reasoning

TL;DR: Build an AI that can use low-level theory to model complex systems on a useful time scale

Abstract: Some of the most exciting and impactful open scientific problems have computational complexity as the limiting factor to an in silico solution, e. g. high–temperature superconductivity and fusion power. Atoms behave according to the well–established laws of quantum mechanics, but as system size grows computations quickly become intractable. This workshop will gather for cross–pollination a diverse group of researchers belonging to difference scientific domains and machine learning approaches. The immediate outcome will be an exchange of ideas, datasets, and crystallized problem statements, all towards the ultimate goal of developing universal AI methods that would be able find efficient and accurate approximations of complex systems from low-level theory. If we solve scale transition, we solve science.

Submission Number: 111