Machine Learning Line Bundle Connections
High Energy Physics - Theory
2022-03-09 v1
Abstract
We study the use of machine learning for finding numerical hermitian Yang-Mills connections on line bundles over Calabi-Yau manifolds. Defining an appropriate loss function and focusing on the examples of an elliptic curve, a K3 surface and a quintic threefold, we show that neural networks can be trained to give a close approximation to hermitian Yang-Mills connections.
Keywords
Cite
@article{arxiv.2110.12483,
title = {Machine Learning Line Bundle Connections},
author = {Anthony Ashmore and Rehan Deen and Yang-Hui He and Burt A. Ovrut},
journal= {arXiv preprint arXiv:2110.12483},
year = {2022}
}