Generating Triangulations and Fibrations with Reinforcement Learning
Abstract
We apply reinforcement learning (RL) to generate fine regular star triangulations of reflexive polytopes, that give rise to smooth Calabi-Yau (CY) hypersurfaces. We demonstrate that, by simple modifications to the data encoding and reward function, one can search for CYs that satisfy a set of desirable string compactification conditions. For instance, we show that our RL algorithm can generate triangulations together with holomorphic vector bundles that satisfy anomaly cancellation and poly-stability conditions in heterotic compactification. Furthermore, we show that our algorithm can be used to search for reflexive subpolytopes together with compatible triangulations that define fibration structures of the CYs.
- Publication:
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arXiv e-prints
- Pub Date:
- May 2024
- DOI:
- 10.48550/arXiv.2405.21017
- arXiv:
- arXiv:2405.21017
- Bibcode:
- 2024arXiv240521017B
- Keywords:
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- High Energy Physics - Theory;
- Mathematical Physics;
- Mathematics - Algebraic Geometry