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Machine Learning Line Bundle Connections

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arxiv 2110.12483 v1 pith:O7DV7W2I submitted 2021-10-24 hep-th

Anthony Ashmore , Rehan Deen , Yang-Hui He , Burt A. Ovrut This is my paper
classification hep-th
keywords connectionshermitianlearninglinemachineyang-millsappropriateapproximation
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Machine Learning the 6d Supergravity Landscape

    hep-th 2025-05 conditional novelty 6.0 of 10

    An autoencoder and two neural classifiers, trained only on anomaly Gram matrices, provide automated clustering, outlier detection, and consistency predictions for millions of 6d supergravity building blocks.

  2. Reproducing Standard Model Fermion Masses and Mixing in String Theory: A Heterotic Line Bundle Study

    hep-th 2025-07 conditional novelty 5.0 of 10

    Explicit heterotic line bundle models on a Calabi-Yau threefold are fitted to reproduce Standard Model quark and charged lepton masses and CKM mixing.

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