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Chapitre D'ouvrage Année : 2023

Deep Learning: Complete Intersection Calabi–Yau Manifolds (Chapter 5)

Résumé

We review advancements in deep learning techniques for complete intersection Calabi-Yau (CICY) 3- and 4-folds, with the aim of understanding better how to handle algebraic topological data with machine learning. We first discuss methodological aspects and data analysis, before describing neural networks architectures. Then, we describe the state-of-the art accuracy in predicting Hodge numbers. We include new results on extrapolating predictions from low to high Hodge numbers, and conversely.
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Dates et versions

cea-04187820 , version 1 (25-08-2023)

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Harold Erbin, Riccardo Finotello. Deep Learning: Complete Intersection Calabi–Yau Manifolds (Chapter 5). Machine Learning in Pure Mathematics and Theoretical Physics, World Scientific, pp.151-181, 2023, 978-1-80061-369-0. ⟨10.1142/9781800613706_0005⟩. ⟨cea-04187820⟩
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