(K)not Machine Learning

Jessica Craven (), Mark Hughes (), Vishnu Jejjala () and Arjun Kar ()
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Jessica Craven: University of the Witwatersrand, Mandelstam Institute for Theoretical Physics, School of Physics, NITheCS, and CoE-MaSS
Mark Hughes: Brigham Young University, Department of Mathematics
Vishnu Jejjala: University of the Witwatersrand, Mandelstam Institute for Theoretical Physics, School of Physics, NITheCS, and CoE-MaSS
Arjun Kar: University of British Columbia, Department of Physics and Astronomy

A chapter in Nankai Symposium on Mathematical Dialogues, 2026, pp 119-127 from Springer

Abstract: Abstract We review recent efforts to machine learn relations between knot invariants. Because these knot invariants have meaning in physics, we explore aspects of Chern–Simons theory and higher dimensional gauge theories. The goal of this work is to translate numerical experiments with Big Data to new analytic results.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-2328-9_13

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DOI: 10.1007/978-981-19-2328-9_13

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