Machine Learning in Physics and Geometry

2023/03/22 by Yang‐Hui He, Elli Heyes, He, Yang-Hui +3 · 2 citations
Earth and Planetary Sciences · #Algebraic Geometry (math.AG) #FOS: Mathematics #FOS: Physical sciences #Geological Modeling and Analysis #High Energy Physics - Theory (hep-th) #Mathematical Physics (math-ph)

paper · pdf · doi:10.48550/arxiv.2303.12626

openalex publication_date 2023/03/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We survey some recent applications of machine learning to problems in geometry and theoretical physics. Pure mathematical data has been compiled over the last few decades by the community and experiments in supervised, semi-supervised and unsupervised machine learning have found surprising success. We thus advocate the programme of machine learning mathematical structures, and formulating conjectures via pattern recognition, in other words using artificial intelligence to help one do mathematics. This is an invited chapter contribution to Elsevier's Handbook of Statistics, Volume 49: Artificial Intelligence edited by S.~G.~Krantz, A.~S.~R.~Srinivasa Rao, and C.~R.~Rao.

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