Abstract: When constructing mathematical models of fundamental physics, one often encounters large parameter spaces that defy systematic scans. In the first part of this talk I will give a quick overview of particle physics and string theory model building and the origin of the large landscapes of theories one encounters. Then I will explain how computational methods, specifically reinforcement learning and genetic algorithms, can be deployed successfully to explore these landscapes and identify models with prescribed properties. In particular, I will show how previously unknown string standard models can be found in this way.
Bio: I am a second year DPhil student, in the Particle Theory subdepartment of the Rudolf Peierls Centre for Theoretical Physics, working under the supervision of Prof Andre Lukas. Before coming to Oxford, I completed an MSci in Theoretical Physics at Royal Holloway in 2019 and Part III of the Mathematical Tripos at Cambridge in 2020.