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The 2-dimensional Ising model on a square lattice is investigated with avariational autoencoder in the non-vanishing field case for the purpose ofextracting the crossover region between the ferromagnetic and paramagneticphases. The encoded latent variable space is found to provide suitable metricsfor tracking the order and disorder in the Ising configurations that extends tothe extraction of a crossover region in a way that is consistent withexpectations. The extracted results achieve an exceptional prediction for thecritical point as well as agreement with previously published results on theconfigurational magnetizations of the model. The performance of this methodprovides encouragement for the use of machine learning to extract meaningfulstructural information from complex physical systems where little a priori datais available.

Deep Learning on the 2-Dimensional Ising Model to Extract the Crossover Region with a Variational Autoencoder