Successful VAEs on many-body data have decoders equivalent to mean-field theories, enabling recovery of parameters like Hopfield patterns from samples alone.
Semi-supervised learn- ing of order parameter in 2D Ising and XY mod- els using conditional variational autoencoders
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
In several physical systems, important properties characterizing the system itself are theoretically related with specific degrees of freedom. Although standard Monte Carlo simulations provide an effective tool to accurately reconstruct the physical configurations of the system, they are unable to isolate the different contributions corresponding to different degrees of freedom. Here we show that unsupervised deep learning can become a valid support to MC simulation, coupling useful insights in the phases detection task with good reconstruction performance. As a testbed we consider the 2D XY model, showing that a deep neural network based on variational autoencoders can detect the continuous Kosterlitz-Thouless (KT) transitions, and that, if endowed with the appropriate constrains, they generate configurations with meaningful physical content.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
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
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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