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This work introduces a latent space method to calculate the demagnetizationreversal process of multigrain permanent magnets. The algorithm consists of twodeep learning models based on neural networks. The embedded Stoner-Wohlfarthmethod is used as a reduced order model for computing samples to train and testthe machine learning approach. The work is a proof of concept of the methodsince the used microstructures are simple and the only varying parameters arethe magnetic anisotropy axes of the hard magnetic grains. A convolutionalautoencoder is used for nonlinear dimensionality reduction, in order to reducethe required amount of training samples for predicting the hysteresis loops. Weenriched the loss function with physical information about the underlyingproblem which increases the accuracy of the machine learning approach. A deeplearning regressor is operating in the latent space of the autoencoder. Thepredictor takes a series of previously encoded magnetization states and outputsthe next magnetization state along the hysteresis curve. To predict thecomplete demagnetization loop, we apply a recursive learning scheme.

Description of collective magnetization processes with machine learning models