Prediction of magnetization dynamics in a reduced dimensional feature space setting utilizing a low-rank kernel method

DOI10.1016/j.jcp.2021.110586OpenAlexW3048917053MaRDI QIDQ2132679

Norbert J. Mauser, Thomas Schrefl, Lukas Exl, Sebastian Schaffer, Dieter Suess

Publication date: 28 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2008.05986

zbMATH Keywords

micromagneticsmachine learningnonlinear model order reductionlow-rank kernel approximationlow-rank kernel principal component analysisNystroem approximation

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Basic methods for problems in optics and electromagnetic theory (78Mxx) General topics in optics and electromagnetic theory (78Axx)

Uses Software

• Scikit
• OOMMF

Cites Work

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• Non-uniform FFT for the finite element computation of the micromagnetic scalar potential
• Kernel methods in machine learning
• A magnetostatic energy formula arising from the \(L^2\)-orthogonal decomposition of the stray field
• The extrapolated explicit midpoint scheme for variable order and step size controlled integration of the Landau-Lifschitz-Gilbert equation
• Learning time-stepping by nonlinear dimensionality reduction to predict magnetization dynamics
• Geometrical integration of Landau-Lifshitz-Gilbert equation based on the mid-point rule
• Understanding Machine Learning

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