arXiv · 2007.13379
Inception Neural Network for Complete Intersection Calabi-Yau 3-folds
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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Harold Erbin, Riccardo Finotello. 2020-07-27. Inception Neural Network for Complete Intersection Calabi-Yau 3-folds. https://doi.org/10.1088/2632-2153%2Fabda61
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