Advanced Numerics for Uncertainty and Bayesian Inference in Science

Description du projet

Pourquoi l’IA doit-elle apprendre le langage de la physique?

La recherche scientifique moderne opère dans deux mondes fracturés: le vaste domaine empirique du big data et de l’apprentissage automatique, et le domaine précis et mécaniste des simulations physiques. Ces deux approches s’opposent, ce qui conduit à des analyses peu efficaces et à un manque de prise en compte de l’incertitude lors de la combinaison de la théorie et de l’observation. Ce projet financé par le CER vise à combler ce fossé. Il présente les méthodes numériques probabilistes comme un langage unificateur, recadrant tout calcul comme une forme d’inférence. Ce cadre holistique permettra aux chercheurs de combiner les données expérimentales avec les lois physiques établies. En développant un langage sémantique pour tous les types de connaissances, le projet va créer une boîte à outils pour la science quantitative, ouvrant la voie à une nouvelle ère de découvertes marquantes.

Objectif

"Scientific knowledge enters computers through data on the one side, and laws of nature – implicit equations like differential equations and symmetries – on the other. They both provide information, empirical and mechanistic, respectively, crucial to the deduction of new insights. But the algorithms that operate on these sources of information stem from different communities and different eras: machine learning – ""big data"" – on the one hand, and simulation methods – high performance computing – on the other. One of the problems that arise from this disconnect is that inferring latent forces that drive dynamical systems from data requires ""shoehorning"" different algorithms together in inefficient optimization loops; another one is that uncertainty from discretization and emulation is not fully tracked.
Probabilistic numerical methods have emerged over the last decade as a holistic view on computation as inference. They provide a unifying language that can leverage empirical and mechanistic information. This proposal outlines a research program to complement and scale probabilistic numerical methods to enrich the quantitative scientist's toolbox along three axes: First, unifying uncertainty from empirical and computational knowledge in one common formalism, which allows the direct and robust combination of simulation and experimentation. Second, developing a rich and practical semantic language for the description of different types of knowledge – mechanistic, empirical, practical. Third, significant computational efficiency gains achieved by managing the computational process globally, instead of as a series of black boxes. Real scientific tasks will provide benchmarks and ensure practical relevance. An open-source software toolbox, complemented by regular summer schools, will ensure that the results reach their audience. As a result, ANUBIS will start a genuinely novel kind of quantitative scientific analysis at the intersection of simulation and machine learning."