Numerical Calabi-Yau metrics from holomorphic networks
High Energy Physics - Theory
2021-05-06 v2 Complex Variables
Computational Physics
Abstract
We propose machine learning inspired methods for computing numerical Calabi-Yau (Ricci flat K\"ahler) 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.
Keywords
Cite
@article{arxiv.2012.04797,
title = {Numerical Calabi-Yau metrics from holomorphic networks},
author = {Michael R. Douglas and Subramanian Lakshminarasimhan and Yidi Qi},
journal= {arXiv preprint arXiv:2012.04797},
year = {2021}
}
Comments
Version accepted by MSML 2021