Deep Learning Complete Intersection Calabi–Yau Manifolds

Published in Machine Learning in Pure Mathematics and Theoretical Physics, pp. 151-181 (2023), 2023

Cite as: H. Erbin, R. Finotello, 'Deep Learning Complete Intersection Calabi–Yau Manifolds', in Machine Learning in Pure Mathematics and Theoretical Physics, pp. 151-181 (2023), World Scientific, edited by Y.-H. He. https://doi.org/10.1088/2632-2153/ac37f7

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 network 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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