Machine-Learning Mathematical Structures Yang-Hui He 1 Merton College, University of Oxford, OX14JD, UK 2 London Institute of Mathematical Sciences, Royal Institution, London, W1S 4BS, UK 3 Department of Mathematics, City, University of London, EC1V 0HB, UK 4 School of Physics, NanKai University, Tianjin, 300071, China
[email protected] Abstract We review, for a general audience, a variety of recent experiments on extracting structure from machine-learning mathematical data that have been compiled over the years. Focusing on supervised machine-learning on labeled data from different fields ranging from geometry to representation theory, from combinatorics to number theory, we present a comparative study of the accuracies on different problems. The paradigm should be useful for conjecture formulation, finding more efficient methods of compu- tation, as well as probing into certain hierarchy of structures in mathematics. Based arXiv:2101.06317v2 [cs.LG] 8 Apr 2021 on various colloquia, seminars and conference talks in 2020, this is a contribution to the launch of the journal \Data Science in the Mathematical Sciences." Contents 1 Introduction & Summary 1 1.1 Bottom-up . .2 1.2 Top-Down . .3 2 Mathematical Data 4 2.1 Methodology . .5 3 Exploring the Landscape of Mathematics 10 3.1 Algebraic Geometry over C ........................... 11 3.2 Representation Theory . 15 3.3 Combinatorics . 18 3.4 Number Theory . 21 4 Conclusions and Outlook 24 1 Introduction & Summary How does one do mathematics? We do not propose this question with the philosophical profundity it deserves, but rather, especially in light of our inexpertise in such foundational issues, merely to draw from some observations on the daily practices of the typical mathe- matician.