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arXiv 2202.07590hep-thcs.LG
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Identifying equivalent Calabi--Yau topologies: A discrete challenge from math and physics for machine learning

University of the Witwatersrand(金山大学);Massachusetts Institute of Technology(麻省理工学院);University of Pennsylvania(宾夕法尼亚大学)

Vishnu Jejjala, Washington Taylor, Andrew Turner

英文摘要:

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.

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