String Model Building, Reinforcement Learning and Genetic Algorithms

2021/11/14 by Abel, Steven, Constantin, Andrei, Harvey, Thomas R. +1 · 1 citation
#FOS: Physical sciences #High Energy Physics - Theory (hep-th)

paper · doi:10.48550/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.

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