Deep learning complete intersection Calabi-Yau manifolds

Abstract

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.

arXiv comment19 pages; match version published in "Machine Learning in Pure Mathematics and Theoretical Physics" (edited by Y.-H. He, World Scientific Press)

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• Calabi-Yau Four/Five/Six-folds as ${\mathbb{P}}_{\text{w}}^n$ Hypersurfaces: Machine Learning, Approximation, and Generation
• Deep Learning Calabi-Yau four folds with hybrid and recurrent neural network architectures