Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections
Machine learning line bundle connections

Line bundle connections

    Neural networks find numerical solutions to Hermitian Yang-Mills equations, a difficult system of PDEs crucial to mathematics and physics.

    Machine learning line bundle connections

    Physics Letters B 827, 136972 (2022)

    A. Ashmore, R. Deen, Y. He, B. A. Ovrut

    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.

    Physics Letters B 827, 136972 (2022)

    A. Ashmore, R. Deen, Y. He, B. A. Ovrut

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      Physics Letters B

      AI classifies space-time

      A neural network learns to classify different types of spacetime in general relativity according to their algebraic Petrov classification.

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      Physics Letters B

      Machine learning Hilbert series

      Neural networks find efficient ways to compute the Hilbert series, an important counting function in algebraic geometry and gauge theory.

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      Physical Review D

      Calabi-Yau anomalies

      Unsupervised machine-learning of the Hodge numbers of Calabi-Yau hypersurfaces detects new patterns with an unexpected linear dependence.