Polytopes and machine learning

Author:

Bao Jiakang12ORCID,He Yang-Hui1234ORCID,Hirst Edward12ORCID,Hofscheier Johannes5ORCID,Kasprzyk Alexander5ORCID,Majumder Suvajit1ORCID

Affiliation:

1. Department of Mathematics, City, University of London, EC1V 0HB, UK

2. London Institute for Mathematical Sciences, Royal Institution, London W1S 4BS, UK

3. Merton College, University of Oxford, OX1 4JD, UK

4. School of Physics, NanKai University, Tianjin 300071, P. R. China

5. School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK

Abstract

We introduce machine learning methodology to the study of lattice polytopes. With supervised learning techniques, we predict standard properties such as volume, dual volume, and reflexivity with accuracies up to 100%. We focus on 2d polygons and 3d polytopes with Plücker coordinates as input, which outperform the usual vertex representation.

Funder

Engineering and Physical Sciences Research Council

Science and Technology Facilities Council

Publisher

World Scientific Pub Co Pte Ltd

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. New Calabi–Yau manifolds from genetic algorithms;International Journal of Data Science in the Mathematical Sciences;2024-05-04

2. Calabi-Yau four-, five-, sixfolds as Pwn hypersurfaces: Machine learning, approximation, and generation;Physical Review D;2024-05-02

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