Machine learning line bundle connections
Ashmore, A., Deen, R., He, Y. 
ORCID: 0000-0002-0787-8380 & Ovrut, B. A. (2022).
Machine learning line bundle connections.
Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics, 827,
article number 136972.
doi: 10.1016/j.physletb.2022.136972
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.
| Publication Type: | Article |
|---|---|
| Additional Information: | © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Publisher Keywords: | Yang–Mills, Machine learning, Connections |
| Subjects: | Q Science > QA Mathematics Q Science > QC Physics |
| Departments: | School of Science & Technology > Department of Mathematics |
| SWORD Depositor: |
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Official URL: https://doi.org/10.1016/j.physletb.2022.136972
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