Machine learning line bundle connections

doi:10.1016/j.physletb.2022.136972

Machine learning line bundle connections

2022-04-01

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DOI：10.1016/j.physletb.2022.136972

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Abstract

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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. (C) 2022 The Author(s). Published by Elsevier B.V.& nbsp;

Keywords:

Yang-Mills

Machine learning

Connections

Machine learning line bundle connections

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