Machine-learned models for magnetic materials
Paweł Leszczyński Kamil Kutorasiński Marcin Szewczyk Jarosław Pawłowski
We present a general framework for modeling materials using deep neural
networks. Material represented by multidimensional characteristics (that mimic
measurements) is 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.