  We present a general framework for modeling power magnetic materials
characteristics using deep neural networks. Magnetic materials represented by
multidimensional characteristics (that mimic measurements) are used to train
the neural autoencoder model in an unsupervised manner. The encoder is trying
to predict the material parameters of a theoretical model, which is then used
in a decoder part. The decoder, using the predicted parameters, reconstructs
the input characteristics. The neural model is trained to capture a
synthetically generated set of characteristics that can cover a broad range of
material behaviors, leading to a model that can generalize on the underlying
physics rather than just optimize the model parameters for a single
measurement. After setting up the model, we prove its usefulness in the complex
problem of modeling magnetic materials in the frequency and current
(out-of-linear range) domains simultaneously, for which we use measured
characteristics obtained for frequency up to $10$ MHz and H-field up to
saturation.
