Speaker
Fabian Ruehle
(Oxford)
Description
In studying the string landscape, we often want to find vacua with specific properties, but do not know how to select the string geometry that gives rise to such vacua. For this reason, we apply reinforcement learning, a semi-supervised approach to machine learning in which the algorithm explores the landscape autonomously while being guided towards models with given properties. We illustrate the approach using examples from heterotic, type II, and F-theory.
