Numerical Calabi-Yau metrics from holomorphic networks

Abstract

We propose machine learning inspired methods for computing numerical Calabi-Yau (Ricci flat Kähler) metrics, and implement them using Tensorflow/Keras. We compare them with previous work, and find that they are far more accurate for manifolds with little or no symmetry. We also discuss issues such as overparameterization and choice of optimization methods.

arXiv commentVersion accepted by MSML 2021

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• Applying machine learning to the Calabi-Yau orientifolds with string vacua
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• Machine Learned Calabi-Yau Metrics and Curvature
• Metric Flows with Neural Networks
• Heterotic Line Bundle Models on Generalized Complete Intersection Calabi Yau Manifolds
• Explicit Soft Supersymmetry Breaking in the Heterotic M-Theory $B-L$ MSSM
• Learning Size and Shape of Calabi-Yau Spaces