An unsupervised neural network learns to compute winding and Chern numbers of two-band insulators by training on topology-preserving deformations of seed Hamiltonians.
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Unsupervised learning using topological data augmentation
An unsupervised neural network learns to compute winding and Chern numbers of two-band insulators by training on topology-preserving deformations of seed Hamiltonians.