Randomized trial demonstrates effective machine learning classification of generalized complete intersection Calabi-Yau manifolds, suggesting advancements in manifold generation.
Generalized complete intersection Calabi-Yau manifold (gCICY) is a new construction of Calabi-Yau manifolds established recently. However, the standard algebraic method to generate new gCICYs is very laborious. Because of this complexity, the number of gCICYs and their classification still remains unknown. In this paper, we try to make some progress in this direction using neural networks. Our results showed that the trained models cannot only get high accuracy on the type (1, 1) and type (2, 1) gCICYs existing in the literature but also achieve a 97% accuracy in predicting new gCICYs not used in the training. This shows that machine learning is an effective method to classify and generate new gCICY.
No takes yet. Share an insight, caveat, or question.
Cui et al. (2023) studied this question.
Synapse has enriched 2 closely related papers on similar clinical questions. Consider them for comparative context: