Machine Learning for Science: Mathematics at the Interface of Data-driven and Mechanistic Modelling

  • Neil Lawrence

    University of Cambridge, UK
  • Jessica Montgomery

    University of Cambridge, UK
  • Bernhard Schölkopf

    Max Planck Institute for Intelligent Systems, Tübingen, Germany

Abstract

Rapid progress in machine learning is enabling scientific advances across a range of disciplines. However, the utility of machine learning for science remains constrained by its current inability to translate insights from data about the dynamics of a system to new scientific knowledge about why those dynamics emerge, as traditionally represented by physical modelling. Mathematics is the interface that bridges data-driven and physical models of the world and can provide a foundation for delivering such knowledge. This workshop convened researchers working across domains with a shared interest in mathematics, machine learning, and their application in the sciences, to explore how tools of mathematics can help build machine learning tools for scientific discovery.

Cite this article

Neil Lawrence, Jessica Montgomery, Bernhard Schölkopf, Machine Learning for Science: Mathematics at the Interface of Data-driven and Mechanistic Modelling. Oberwolfach Rep. 20 (2023), no. 2, pp. 1453–1484

DOI 10.4171/OWR/2023/26