Machine Learning Algebraic Geometry for Physics

• URL: http://arxiv.org/abs/2204.10334v1
• Date: Thu, 21 Apr 2022 18:00:03 GMT
• Title: Machine Learning Algebraic Geometry for Physics
• Authors: Jiakang Bao, Yang-Hui He, Elli Heyes, Edward Hirst
• Abstract summary: This chapter is a contribution to the book Machine learning and Algebraic Geometry, edited by A. Kasprzyk and A. Kasprzyk et al.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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