String Model Building, Reinforcement Learning and Genetic Algorithms
arXiv:2111.07333
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.
9 pages Latex, 4 figures, based on a talk given by AL at the Nankai Symposium on Mathematical Dialogues, 2021
References in corpus (6)
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