  The 2-dimensional Ising model on a square lattice is investigated with a
variational autoencoder in the non-vanishing field case for the purpose of
extracting the crossover region between the ferromagnetic and paramagnetic
phases. The encoded latent variable space is found to provide suitable metrics
for tracking the order and disorder in the Ising configurations that extends to
the extraction of a crossover region in a way that is consistent with
expectations. The extracted results achieve an exceptional prediction for the
critical point as well as agreement with previously published results on the
configurational magnetizations of the model. The performance of this method
provides encouragement for the use of machine learning to extract meaningful
structural information from complex physical systems where little a priori data
is available.
