Machine Learning Algebraic Geometry for Physics
High Energy Physics - Theory
2022-04-25 v1 Algebraic Geometry
Machine Learning
Abstract
We review some recent applications of machine learning to algebraic geometry and physics. Since problems in algebraic geometry can typically be reformulated as mappings between tensors, this makes them particularly amenable to supervised learning. Additionally, unsupervised methods can provide insight into the structure of such geometrical data. At the heart of this programme is the question of how geometry can be machine learned, and indeed how AI helps one to do mathematics. This is a chapter contribution to the book Machine learning and Algebraic Geometry, edited by A. Kasprzyk et al.
Keywords
Cite
@article{arxiv.2204.10334,
title = {Machine Learning Algebraic Geometry for Physics},
author = {Jiakang Bao and Yang-Hui He and Elli Heyes and Edward Hirst},
journal= {arXiv preprint arXiv:2204.10334},
year = {2022}
}
Comments
32 pages, 25 figures. Contribution to Machine learning and Algebraic Geometry, edited by A. Kasprzyk et al