论文标题
通用的完整交叉点上的机器学习卡拉比（Calabi-Yau）歧管
Machine Learning on generalized Complete Intersection Calabi-Yau Manifolds
论文作者
论文摘要
通用的完整交叉点Calabi-yau歧管（GCICY）是最近建立的卡拉比远流形的新结构。但是，使用标准代数方法的新GCICY产生非常费力。由于这种复杂性，GCICYS及其分类的数量仍然未知。在本文中，我们尝试使用神经网络在这个方向上取得了一些进展。结果表明，我们训练有素的模型可以在文献中现有类型的$（1,1）$（1,1）$（1,1）$（1,1）$（1,1）$（2,1）$ GCICY上具有很高的精度。此外，他们可以在预测与培训和测试的新型GCICY预测新的GCICY方面达到$ 97 \％$的精度。这表明机器学习可能是对新的gcicy进行分类和生成新的方法的有效方法。
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.
