EMPG - Machine Learning for Calabi-Yau Compactifications

Abstract

Calabi-Yau metrics and hermitian Yang-Mills connections have played a key role in both mathematics and physics in recent decades, and are particularly important for deriving semi-realistic models of particle physics from string theory. Unfortunately, explicit expressions for these objects are few and far between, leaving us unable, for example, to compute particle masses or couplings in string models. I will review recent progress on using machine learning techniques to compute these quantities numerically, with a particular focus on the example of computing gauge connections on line bundles.

Anthony Ashmore

Marie Skłodowska-Curie Fellow