Identifying Equivalent Calabi–Yau Topologies: A Discrete Challenge from Math and Physics for Machine Learning

Vishnu Jejjala (), Washington Taylor () and Andrew P. Turner ()
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Vishnu Jejjala: Mandelstam Institute for Theoretical Physics, University of the Witwatersrand, School of Physics, NITheCS, and CoE-MaSS
Washington Taylor: Center for Theoretical Physics, NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Massachusetts Institute of Technology, Department of Physics
Andrew P. Turner: University of Pennsylvania, Department of Physics and Astronomy

A chapter in Nankai Symposium on Mathematical Dialogues, 2026, pp 197-204 from Springer

Abstract: Abstract We review briefly the characteristic topological data of Calabi–Yau threefolds and focus on the question of when two threefolds are equivalent through related topological data. This provides an interesting test case for machine learning methodology in discrete mathematics problems motivated by physics.

Date: 2026
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DOI: 10.1007/978-981-19-2328-9_23

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