Machine Learning on generalized Complete Intersection Calabi-Yau Manifolds
This addresses a complex problem in mathematical physics for researchers studying Calabi-Yau manifolds, but it is incremental as it applies an existing method to a new dataset.
The paper tackled the laborious generation and classification of generalized Complete Intersection Calabi-Yau Manifolds (gCICYs) using neural networks, achieving 97% precision in predicting new gCICYs generated differently from training data.
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.