Learning Size and Shape of Calabi-Yau Spaces

• URL: http://arxiv.org/abs/2111.01436v1
• Date: Tue, 2 Nov 2021 08:48:53 GMT
• Title: Learning Size and Shape of Calabi-Yau Spaces
• Authors: Magdalena Larfors, Andre Lukas, Fabian Ruehle, Robin Schneider
• Abstract summary: We present a new machine learning library for computing metrics of string compactification spaces. We benchmark the performance on Monte-Carlo sampled integrals against previous numerical approximations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a new machine learning library for computing metrics of string compactification spaces. We benchmark the performance on Monte-Carlo sampled integrals against previous numerical approximations and find that our neural networks are more sample- and computation-efficient. We are the first to provide the possibility to compute these metrics for arbitrary, user-specified shape and size parameters of the compact space and observe a linear relation between optimization of the partial differential equation we are training against and vanishing Ricci curvature.

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