Identifying equivalent Calabi--Yau topologies: A discrete challenge from math and physics for machine learning
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 36 references that are in the map. First posted on arXiv 15 Feb 2022.
Builds on
10 of 36 references.
References · 10
- 2016Why does deep and cheap learning work so well?cited by 100
- 2018Machine Learning CICY Threefoldscited by 22
- 2017Deep-Learning the Landscapecited by 21
- 2018The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learningcited by 19
- 2020Machine learning for complete intersection Calabi-Yau manifolds: a methodological studycited by 17
- 2020Machine Learning Calabi-Yau Four-foldscited by 17
- 2021Machine Learning Calabi-Yau Hypersurfacescited by 15
- 2020Inception Neural Network for Complete Intersection Calabi-Yau 3-foldscited by 14
- 2021Deep multi-task mining Calabi-Yau four-foldscited by 11
- 2021Machine Learning Kreuzer--Skarke Calabi--Yau Threefoldscited by 8
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Citing papers per year: 2022: 2.
2 papers.
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