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Article Dans Une Revue Machine Learning: Science and Technology Année : 2021

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

Résumé

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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cea-04082047 , version 1 (26-04-2023)

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Harold Erbin, Riccardo Finotello. Inception neural network for complete intersection Calabi-Yau 3-folds. Machine Learning: Science and Technology, 2021, 2 (2), pp.02LT03. ⟨10.1088/2632-2153/abda61⟩. ⟨cea-04082047⟩
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