String Model Building, Reinforcement Learning and Genetic Algorithms

2021/11/14 by Steven Abel, Andrei Constantin, Abel, Steven +5 · 2 citations
Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Theory (hep-th) #hep-th

paper · pdf · doi:10.48550/arxiv.2111.07333

9 pages Latex, 4 figures, based on a talk given by AL at the Nankai Symposium on Mathematical Dialogues, 2021

arxiv created 2021/11/14 · arxiv updated 2021/11/16

Abstract

We investigate reinforcement learning and genetic algorithms in the context of heterotic Calabi-Yau models with monad bundles. Both methods are found to be highly efficient in identifying phenomenologically attractive three-family models, in cases where systematic scans are not feasible. For monads on the bi-cubic Calabi-Yau either method facilitates a complete search of the environment and leads to similar sets of previously unknown three-family models.

Cited by

• Rediscovering the Standard Model with AI
• Transforming Calabi-Yau Constructions: Generating New Calabi-Yau Manifolds with Transformers

Related

• Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning
• Generating Triangulations and Fibrations with Reinforcement Learning
• Probing the Structure of String Theory Vacua with Genetic Algorithms and Reinforcement Learning
• Exploring Line Bundle Standard Models with Transformers
• Lectures on the Calabi-Yau Landscape