Inception Neural Network for Complete Intersection Calabi–Yau 3-folds

We introduce a neural network inspired by Google’s Inception model to compute the Hodge number $h^{1,1}$ of Complete Intersection Calabi-Yau 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.

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