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Polytopes and Machine Learning

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arxiv 2109.09602 v1 pith:S2ZQMVDD submitted 2021-09-15 math.CO hep-thmath.AG

Jiakang Bao , Yang-Hui He , Edward Hirst , Johannes Hofscheier , Alexander Kasprzyk , Suvajit Majumder This is my paper
classification math.COhep-thmath.AG
keywords learningpolytopesmachinevolumeaccuraciescoordinatesdualfocus
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We introduce machine learning methodology to the study of lattice polytopes. With supervised learning techniques, we predict standard properties such as volume, dual volume, reflexivity, etc, with accuracies up to 100%. We focus on 2d polygons and 3d polytopes with Pl\"ucker coordinates as input, which out-perform the usual vertex representation.

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Cited by 1 Pith paper

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  1. Machine Learning Free Quotients of CICYs

    hep-th 2025-08 conditional novelty 6.0 of 10

    Machine-learning classifiers, especially a multi-head attention model, correctly identify almost all free Z2, Z3, Z4, and Z2xZ2 quotients of CICYs on held-out manifolds, with only three missed Z2xZ2 cases.

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