Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Li, Y; Lei, G; Bramerdorfer, G; Peng, S; Sun, X; Zhu, J

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Li, Y Lei, G

Bramerdorfer, G Peng, S Sun, X Zhu, J

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Applied Sciences, 2021, 11, (4), pp. 1-24
Issue Date:: 2021-02-11

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Field	Value	Language
dc.contributor.author	Li, Y
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.contributor.author	Bramerdorfer, G
dc.contributor.author	Peng, S
dc.contributor.author	Sun, X
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.date.accessioned	2022-01-30T09:50:27Z
dc.date.available	2022-01-30T09:50:27Z
dc.date.issued	2021-02-11
dc.identifier.citation	Applied Sciences, 2021, 11, (4), pp. 1-24
dc.identifier.issn	1454-5101
dc.identifier.issn	2076-3417
dc.identifier.uri	http://hdl.handle.net/10453/153875
dc.description.abstract	This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Applied Sciences
dc.relation.isbasedon	10.3390/app11041627
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions
dc.type	Journal Article
utslib.citation.volume	11
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2022-01-30T09:50:21Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	4

Abstract:

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

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http://hdl.handle.net/10453/153875