Machine learning methods for the prediction of micromagnetic magnetization dynamics

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

doi:10.1109/TMAG.2021.3095251

Machine learning methods for the prediction of micromagnetic magnetization dynamics

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

Publications: Contribution to journal › Article › Peer Reviewed

Original language	English
Article number	7100506
Number of pages	6
Journal	IEEE Transactions on Magnetics
Volume	58
Issue number	2
DOIs	https://doi.org/10.1109/TMAG.2021.3095251
Publication status	Published - 1 Feb 2022

Austrian Fields of Science 2012

101014 Numerical mathematics
103043 Computational physics
102019 Machine learning

Keywords

computational micromagnetism
Computational modeling
deep neural networks
Kernel
kernel methods
low-rank approximation
Magnetic domains
Magnetization
Mathematical model
nonlinear model order and dimensionality reduction
Principal component analysis
regularized autoencoders
Training
deep neural networks (NNs)
regularized autoencoders (AEs)
Computational micromagnetism

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10.1109/TMAG.2021.3095251

Cite this

@article{473ae11024ea46a588271d42699ab65b,

title = "Machine learning methods for the prediction of micromagnetic magnetization dynamics",

keywords = "computational micromagnetism, Computational modeling, deep neural networks, Kernel, kernel methods, low-rank approximation, Magnetic domains, Magnetization, Mathematical model, nonlinear model order and dimensionality reduction, Principal component analysis, regularized autoencoders, Training, deep neural networks (NNs), regularized autoencoders (AEs), Computational micromagnetism",

author = "Sebastian Schaffer and Mauser, {Norbert J.} and Thomas Schrefl and Dieter Suess and Lukas Exl",

year = "2022",

month = feb,

day = "1",

doi = "10.1109/TMAG.2021.3095251",

language = "English",

volume = "58",

journal = "IEEE Transactions on Magnetics",

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publisher = "IEEE",

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TY - JOUR

T1 - Machine learning methods for the prediction of micromagnetic magnetization dynamics

AU - Schaffer, Sebastian

AU - Mauser, Norbert J.

AU - Schrefl, Thomas

AU - Suess, Dieter

AU - Exl, Lukas

PY - 2022/2/1

Y1 - 2022/2/1

KW - computational micromagnetism

KW - Computational modeling

KW - deep neural networks

KW - Kernel

KW - kernel methods

KW - low-rank approximation

KW - Magnetic domains

KW - Magnetization

KW - Mathematical model

KW - nonlinear model order and dimensionality reduction

KW - Principal component analysis

KW - regularized autoencoders

KW - Training

KW - deep neural networks (NNs)

KW - regularized autoencoders (AEs)

KW - Computational micromagnetism

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U2 - 10.1109/TMAG.2021.3095251

DO - 10.1109/TMAG.2021.3095251

M3 - Article

AN - SCOPUS:85110911147

SN - 0018-9464

VL - 58

JO - IEEE Transactions on Magnetics

JF - IEEE Transactions on Magnetics

IS - 2

M1 - 7100506

ER -

Machine learning methods for the prediction of micromagnetic magnetization dynamics

Austrian Fields of Science 2012

Keywords

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CD-Labor Advanced Magnetic Sensing and Materials

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Machine learning methods for the prediction of micromagnetic magnetization dynamics

Austrian Fields of Science 2012

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CD-Labor Advanced Magnetic Sensing and Materials

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