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

• URL: http://arxiv.org/abs/2007.13379v2
• Date: Tue, 16 Feb 2021 08:03:43 GMT
• Title: Inception Neural Network for Complete Intersection Calabi-Yau 3-folds
• Authors: Harold Erbin, Riccardo Finotello
• Abstract summary: We introduce a neural network inspired by Google's Inception model to compute the Hodge number $h1,1$ 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. 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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