Abstract

In studying the vacua of string theory, researchers often want to find solutions with specific properties, but do not know how to select the string geometry that gives rise to such vacua. For this reason, the speaker applies reinforcement learning, a semi-supervised approach to machine learning, in which the algorithm explores the landscape of string solutions autonomously while being guided towards models with given properties. In this talk, he will give an introduction to reinforcement learning and illustrate the mechanism in several examples from type II string theory, heterotic string theory, and F-theory.

About the program

For more information, please refer to the program website at http://iasprogram.ust.hk/particle_theory.

The seminar is free and open to all. Seating is on a first come, first served basis.

HKUST Jockey Club Institute for Advanced Study
Enquiries: ias@ust.hk / 2358 5912
http://ias.ust.hk