Mathematical data science

Advances in Theoretical and Mathematical Physics 2026 Open access

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Authors: Michael R. Douglas, Kee Lee
DOI: 10.4310/atmp.260412174613
Cited by: 0

Abstract

This article explores how machine learning, through a paradigm of mathematical data science, can enable the discovery of new mathematical structures by analyzing datasets of mathematical objects, illustrated by case studies on murmurations in number theory and partition loadings related to Kronecker coefficients.

Fields of Study & Subjects

Big Data and Business Intelligence

Publication details

Journal / source
Advances in Theoretical and Mathematical Physics
Volume
29
Issue
8
Pages
2305–2325
Publication date
1 January 2026
Language
EN