Identifying equivalent Calabi--Yau topologies: A discrete challenge from math and physics for machine learning

Andrew Turner; Vishnu Jejjala; Washington Taylor

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Identifying equivalent Calabi--Yau topologies: A discrete challenge from math and physics for machine learning

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arxiv 2202.07590 v1 pith:N7BQSCCQ submitted 2022-02-15 hep-th cs.LG

Identifying equivalent Calabi--Yau topologies: A discrete challenge from math and physics for machine learning

Vishnu Jejjala , Washington Taylor , Andrew Turner This is my paper

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keywords calabi--yaudatadiscreteequivalentlearningmachinephysicsthreefolds

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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.

Identifying equivalent Calabi--Yau topologies: A discrete challenge from math and physics for machine learning

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