Calabi Yau

Calabi-Yau manifolds are complex geometric spaces with significant implications in string theory and related areas of physics. Current research heavily utilizes machine learning, particularly deep learning architectures like convolutional and recurrent neural networks, and genetic algorithms, to efficiently analyze and predict properties of these manifolds, such as Hodge numbers and Euler characteristics, and to approximate their complex metrics. This work is crucial for advancing our understanding of string theory compactifications and related physical phenomena, as well as for developing new mathematical tools for exploring high-dimensional geometries.