Magnetostatics and micromagnetics with physics informed neural networks

Alexander Kovacs; Lukas Exl; Alexander Kornell; Johann Fischbacher; Markus Hovorka; Markus Gusenbauer; Leoni Breth; Harald Oezelt; Dirk Praetorius; Dieter Suess; Thomas Schrefl

doi:10.1016/j.jmmm.2021.168951

Magnetostatics and micromagnetics with physics informed neural networks

Alexander Kovacs, Lukas Exl, Alexander Kornell, Johann Fischbacher, Markus Hovorka, Markus Gusenbauer, Leoni Breth, Harald Oezelt, Dirk Praetorius, Dieter Suess, Thomas Schrefl (Corresponding author)

Publications: Contribution to journal › Article › Peer Reviewed

Original language	English
Article number	168951
Number of pages	12
Journal	Journal of Magnetism and Magnetic Materials
Volume	548
Early online date	1 Jan 2022
DOIs	https://doi.org/10.1016/j.jmmm.2021.168951
Publication status	Published - 15 Apr 2022

Austrian Fields of Science 2012

103015 Condensed matter

Keywords

Inverse problems
Magnetostatics
Neural network
Ritz method

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10.1016/j.jmmm.2021.168951Licence: CC BY 4.0

https://arxiv.org/abs/2106.03362Licence: Other

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title = "Magnetostatics and micromagnetics with physics informed neural networks",

keywords = "Inverse problems, Magnetostatics, Neural network, Ritz method",

author = "Alexander Kovacs and Lukas Exl and Alexander Kornell and Johann Fischbacher and Markus Hovorka and Markus Gusenbauer and Leoni Breth and Harald Oezelt and Dirk Praetorius and Dieter Suess and Thomas Schrefl",

note = "Funding Information: The financial support by the Austrian Federal Ministry for Digital and Economic Affairs , the National Foundation for Research, Technology and Development, Austria and the Christian Doppler Research Association, Austria is gratefully acknowledged. L.E. and D.P. acknowledge support by the Austrian Science Fund (FWF) under grant No. P31140-N32 and grant No. F65 , respectively. Publisher Copyright: {\textcopyright} 2022 The Authors Copyright: Copyright 2022 Elsevier B.V., All rights reserved.",

year = "2022",

month = apr,

day = "15",

doi = "10.1016/j.jmmm.2021.168951",

language = "English",

volume = "548",

journal = "Journal of Magnetism and Magnetic Materials",

issn = "0304-8853",

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TY - JOUR

T1 - Magnetostatics and micromagnetics with physics informed neural networks

AU - Kovacs, Alexander

AU - Exl, Lukas

AU - Kornell, Alexander

AU - Fischbacher, Johann

AU - Hovorka, Markus

AU - Gusenbauer, Markus

AU - Breth, Leoni

AU - Oezelt, Harald

AU - Praetorius, Dirk

AU - Suess, Dieter

AU - Schrefl, Thomas

N1 - Funding Information: The financial support by the Austrian Federal Ministry for Digital and Economic Affairs , the National Foundation for Research, Technology and Development, Austria and the Christian Doppler Research Association, Austria is gratefully acknowledged. L.E. and D.P. acknowledge support by the Austrian Science Fund (FWF) under grant No. P31140-N32 and grant No. F65 , respectively. Publisher Copyright: © 2022 The Authors Copyright: Copyright 2022 Elsevier B.V., All rights reserved.

PY - 2022/4/15

Y1 - 2022/4/15

KW - Inverse problems

KW - Magnetostatics

KW - Neural network

KW - Ritz method

UR - http://www.scopus.com/inward/record.url?scp=85122498553&partnerID=8YFLogxK

U2 - 10.1016/j.jmmm.2021.168951

DO - 10.1016/j.jmmm.2021.168951

M3 - Article

AN - SCOPUS:85122498553

SN - 0304-8853

VL - 548

JO - Journal of Magnetism and Magnetic Materials

JF - Journal of Magnetism and Magnetic Materials

M1 - 168951

ER -

Magnetostatics and micromagnetics with physics informed neural networks

Austrian Fields of Science 2012

Keywords

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CD-Labor Advanced Magnetic Sensing and Materials

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Magnetostatics and micromagnetics with physics informed neural networks

Austrian Fields of Science 2012

Keywords

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CD-Labor Advanced Magnetic Sensing and Materials

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