Calabi-Yau Metrics, Energy Functionals and Machine-Learning
High Energy Physics - Theory
2022-09-07 v1 Machine Learning
Algebraic Geometry
Abstract
We apply machine learning to the problem of finding numerical Calabi-Yau metrics. We extend previous work on learning approximate Ricci-flat metrics calculated using Donaldson's algorithm to the much more accurate "optimal" metrics of Headrick and Nassar. We show that machine learning is able to predict the K\"ahler potential of a Calabi-Yau metric having seen only a small sample of training data.
Cite
@article{arxiv.2112.10872,
title = {Calabi-Yau Metrics, Energy Functionals and Machine-Learning},
author = {Anthony Ashmore and Lucille Calmon and Yang-Hui He and Burt A. Ovrut},
journal= {arXiv preprint arXiv:2112.10872},
year = {2022}
}
Comments
7 pages, 5 figures