  We introduce a new method to identify phase boundaries in physical systems.
It is based on training a predictive model such as a neural network to infer a
physical system's parameters from its state. The deviation of the inferred
parameters from the underlying correct parameters will be most susceptible and
diverge maximally in the vicinity of phase boundaries. Therefore, peaks in the
divergence of the model's predictions are used as indication of phase
transitions. Our method is applicable for phase diagrams of arbitrary parameter
dimension and without prior information about the phases. Application to both
the two-dimensional Ising model and the dissipative Kuramoto-Hopf model show
promising results.
