Learning magnetization dynamics

Alexander Kovacs; Johann Fischbacher; Harald Oezelt; Markus Gusenbauer; Lukas Exl; Florian Bruckner; Dieter Suess; Thomas Schrefl

doi:10.1016/j.jmmm.2019.165548

arXiv · 1903.09499

Learning magnetization dynamics

Alexander Kovacs·Johann Fischbacher·Harald Oezelt·Markus Gusenbauer·Lukas Exl·Florian Bruckner·Dieter Suess·Thomas Schrefl

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Abstract

Deep neural networks are used to model the magnetization dynamics in magnetic thin film elements. The magnetic states of a thin film element can be represented in a low dimensional space. With convolutional autoencoders a compression ratio of 1024:1 was achieved. Time integration can be performed in the latent space with a second network which was trained by solutions of the Landau-Lifshitz-Gilbert equation. Thus the magnetic response to an external field can be computed quickly.

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Alexander Kovacs, Johann Fischbacher, Harald Oezelt, Markus Gusenbauer, Lukas Exl, Florian Bruckner, Dieter Suess, Thomas Schrefl. 2019-03-22. Learning magnetization dynamics. https://doi.org/10.1016/j.jmmm.2019.165548

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