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. 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.

中文翻译：

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初始神经网络用于完整的相交Calabi–Yau 3折

我们引入了一个受Google的Inception模型启发的神经网络，以计算完整交叉点Calabi–Yau（CICY）3倍的霍奇数h ^1,1。这种体系结构大大提高了现有结果的预测准确性，仅通过30％的训练数据就可以提供97％的准确性。使用80％的数据进行训练时，准确性会上升到99％。这证明了神经网络是研究纯数学和弦论中几何学方面的宝贵资源。