String Model Building, Reinforcement Learning and Genetic Algorithms
High Energy Physics - Theory
2021-11-16 v1
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.
Keywords
Cite
@article{arxiv.2111.07333,
title = {String Model Building, Reinforcement Learning and Genetic Algorithms},
author = {Steven Abel and Andrei Constantin and Thomas R. Harvey and Andre Lukas},
journal= {arXiv preprint arXiv:2111.07333},
year = {2021}
}
Comments
9 pages Latex, 4 figures, based on a talk given by AL at the Nankai Symposium on Mathematical Dialogues, 2021