Polytopes and machine learning-Reference-Cited by-同舟云学术

Polytopes and machine learning

Published:2023-12 Issue:02 Volume:01 Page:181-211
ISSN:2810-9392
Container-title:International Journal of Data Science in the Mathematical Sciences
language:en
Short-container-title:Int. J. Data. Sc. Math. Sc.

Author:

Bao Jiakang¹²^ORCID,He Yang-Hui¹²³⁴^ORCID,Hirst Edward¹²^ORCID,Hofscheier Johannes⁵^ORCID,Kasprzyk Alexander⁵^ORCID,Majumder Suvajit¹^ORCID

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

Link

https://www.worldscientific.com/doi/pdf/10.1142/S281093922350003X

Reference35 articles.

1. Estimating Calabi-Yau hypersurface and triangulation counts with equation learners

2. A Calabi-Yau database: threefolds constructed from the Kreuzer-Skarke list

3. Quiver mutations, Seiberg duality, and machine learning

4. Neurons on amoebae

5. Hilbert series, machine learning, and applications to physics

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