Machine learning for complete intersection Calabi-Yau manifolds - CEA - Commissariat à l’énergie atomique et aux énergies alternatives Access content directly
Conference Poster Year : 2022

Machine learning for complete intersection Calabi-Yau manifolds

Abstract

We describe the recent developments in using machine learning techniques to compute Hodge numbers of complete intersection Calabi-Yau (CICY) 3- and 4-folds. The main motivation is to understand how to study data from algebraic geometry and solve problems relevant for string theory with machine learning. We describe the state-of-the art methods which reach near-perfect accuracy for several Hodge numbers, and discuss extrapolating from low to high Hodge numbers, and conversely.
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Dates and versions

cea-04082321 , version 1 (26-04-2023)

Identifiers

  • HAL Id : cea-04082321 , version 1

Cite

Harold Erbin, Riccardo Finotello, Mohamed Tamaazousti. Machine learning for complete intersection Calabi-Yau manifolds. NeurIPS 2022 - The 36th conference on Neural Information Processing Systems, Dec 2022, New Orléans, United States. , 2022. ⟨cea-04082321⟩
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