An extended WGAN generates spin configurations from topological charge distributions that match magnetization, susceptibility, helicity modulus, and spin correlations below the Kosterlitz-Thouless transition in the 2D XY model, while showing deviations in specific heat and subtle structural features
Improved training of Wasserstein GANs
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Reconstruction of spin structures from topological charge distributions via generative neural network systems
An extended WGAN generates spin configurations from topological charge distributions that match magnetization, susceptibility, helicity modulus, and spin correlations below the Kosterlitz-Thouless transition in the 2D XY model, while showing deviations in specific heat and subtle structural features