Probing the Structure of String Theory Vacua with Genetic Algorithms and Reinforcement Learning

2021/11/22 by Alex Cole, Cole, Alex, Sven Krippendorf +5 · 3 citations
Computer Science · #Advanced Data Storage Technologies #Algorithms and Data Compression #Computational Physics and Python Applications #FOS: Physical sciences #High Energy Physics - Theory (hep-th) #Parallel Computing and Optimization Techniques

paper · pdf · doi:10.48550/arxiv.2111.11466

openalex publication_date 2021/11/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Identifying string theory vacua with desired physical properties at low energies requires searching through high-dimensional solution spaces - collectively referred to as the string landscape. We highlight that this search problem is amenable to reinforcement learning and genetic algorithms. In the context of flux vacua, we are able to reveal novel features (suggesting previously unidentified symmetries) in the string theory solutions required for properties such as the string coupling. In order to identify these features robustly, we combine results from both search methods, which we argue is imperative for reducing sampling bias.

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• Reinforcement Learning techniques for the flavor problem in particle physics
• Transforming Calabi-Yau Constructions: Generating New Calabi-Yau Manifolds with Transformers
• Solving inverse problems of Type IIB flux vacua with conditional generative models

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