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.

翻译：我们回顾了深度学习方法在完全交Calabi-Yau (CICY)三维与四维流形中的研究进展，旨在更深入理解如何利用机器学习处理代数拓扑数据。首先讨论方法论与数据分析层面，继而描述神经网络架构。随后阐述预测Hodge数的最新精度水平，并纳入关于从低Hodge数向高Hodge数外推预测及其反向过程的新结果。