Machine-learned models for magnetic materials

arXiv:2401.00072v2 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv autoencoder cond-mat.mtrl-sci cs.lg encoder framework general machine material materials modeling multidimensional networks neural networks parameters power replace train type unsupervised