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Universes as big data

Yang-Hui He

Yang-Hui He

Merton College, University of Oxford, Oxford OX1 4JD, UK

London Institute, Royal Institution of GB, 21 Albemarle Street, London W1S 4BS, UK

Department of Mathematics, City, University of London, London EC1V 0HB, UK

School of Physics, NanKai University, Tianjin, 300071, P. R. China

E-mail Address: [email protected]

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https://doi.org/10.1142/S0217751X21300179Cited by:2 (Source: Crossref)

Abstract

In this paper, we briefly overview how, historically, string theory led theoretical physics first to precise problems in algebraic and differential geometry, and thence to computational geometry in the last decade or so, and now, in the last few years, to data science. Using the Calabi–Yau landscape — accumulated by the collaboration of physicists, mathematicians and computer scientists over the last four decades — as a starting-point and concrete playground, we review some recent progress in machine-learning applied to the sifting through of possible universes from compactification, as well as wider problems in geometrical engineering of quantum field theories. In parallel, we discuss the program in machine-learning mathematical structures and address the tantalizing question of how it helps doing mathematics, ranging from mathematical physics, to geometry, to representation theory, to combinatorics and to number theory.

Invited review for IJMPA, based on various colloquia, seminars and conference talks in the 2019–2020 academic year.

Keywords:

PACS: 11.25.-w, 11.25.Mj, 11.25.Wx, 02.40.-k

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Vol. 36, No. 29

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Received 28 September 2021

Accepted 6 October 2021

Published: 8 November 2021

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Universes as big data

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Universes as big data

Abstract

References

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