Identifying equivalent Calabi--Yau topologies: A discrete challenge from math and physics for machine learning
High Energy Physics - Theory
2022-02-16 v1 Machine Learning
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.
Keywords
Cite
@article{arxiv.2202.07590,
title = {Identifying equivalent Calabi--Yau topologies: A discrete challenge from math and physics for machine learning},
author = {Vishnu Jejjala and Washington Taylor and Andrew Turner},
journal= {arXiv preprint arXiv:2202.07590},
year = {2022}
}
Comments
6 pages, 3 figures; Contribution to proceedings of 2021 Nankai symposium on Mathematical Dialogues in celebration of S. S. Chern's 110th anniversary