Machine Learning on generalized Complete Intersection Calabi-Yau Manifolds
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Abstract (arXiv)
Generalized Complete Intersection Calabi-Yau Manifold (gCICY) is a new construction of Calabi-Yau manifolds established recently. However, the generation of new gCICYs using standard algebraic method is very laborious. Due to this complexity, the number of gCICYs and their classification still remain unknown. In this paper, we try to make some progress in this direction using neural network. The results showed that our trained models can have a high precision on the existing type (1,1) and type (2,1) gCICYs in the literature. Moreover, They can achieve a 97% precision in predicting new gCICY which is generated differently from those used for training and testing. This shows that machine learning could be an effective method to classify and generate new gCICY.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 61 references that are in the map. First posted on arXiv 21 Sep 2022.
Builds on
12 of 61 references.
References · 12
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Citing papers per year: 2023: 1.
1 paper.