Inception neural network for complete intersection Calabi–Yau 3-folds

Abstract

Abstract 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.