Search

Workshop

Universes as Bigdata: Physics, Geometry and Machine-Learning

  • Yang-Hui He (London Institute for Mathematical Sciences & Merton College, Oxford University, London, United Kingdom)
E1 05 (Leibniz-Saal)

Abstract

The search for the Theory of Everything has led to superstring theory, which then led physics, first to algebraic/differential geometry/topology, and then to computational geometry, and now to data science.

With a concrete playground of the geometric landscape, accumulated by the collaboration of physicists, mathematicians and computer scientists over the last 4 decades, we show how the latest techniques in machine-learning can help explore problems of interest to theoretical physics and to pure mathematics.

At the core of our programme is the question: how can AI help us with mathematics?

Tabea Bacher

Max-Planck-Institut für Mathematik in den Naturwissenschaften Contact via Mail

Christiane Görgen

Max Planck Institute for Mathematics in the Sciences and Universität Leipzig

Martina Juhnke-Kubitzke

Universität Osnabrück

Thomas Kahle

Otto-von-Guericke-Universität

Lars Kastner

Technische Universität Berlin

Raman Sanyal

Goethe Universität Frankfurt and Freie Universität Berlin

Christian Stump

Ruhr-Universität Bochum