论文标题
完整交叉点的神经网络Calabi-yau 3倍
Inception Neural Network for Complete Intersection Calabi-Yau 3-folds
论文作者
论文摘要
我们介绍了一个受Google Inception模型启发的神经网络，以计算完整相交的Calabi-Yau（Cicy）3倍的Hodge Number $ H^{1,1} $。这种体系结构在很大程度上提高了预测的准确性，而不是现有结果的准确性，仅30％的数据才能进行培训的97％的准确性。此外，使用80％的数据进行培训时，精度将攀升至99％。这证明神经网络是研究纯数学和弦理论中几何方面的宝贵资源。
We introduce a neural network inspired by Google's Inception model to compute the Hodge number $h^{1,1}$ of complete intersection Calabi-Yau (CICY) 3-folds. This architecture improves largely the accuracy of the predictions over existing results, giving already 97% of accuracy with just 30% of the data for training. Moreover, accuracy climbs to 99% when using 80% of the data for training. This proves that neural networks are a valuable resource to study geometric aspects in both pure mathematics and string theory.
