Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Issue title: Deep learning for analysis and synthesis in electromagnetics
Guest editors: Maria Evelina Mognaschi
Article type: Review Article
Authors: Lowther, David A.a;
Affiliations: [a] McGill University, Montreal, Canada E-mail: david.lowther@mcgill.ca
Correspondence: [*] E-mail: david.lowther@mcgill.ca
Abstract: Designing an electromagnetic device, as with many other devices, is an inverse problem. The issue is that the performance and some constraints on the inputs are provided but the solution to the design problem is non-unique. Additionally, conventionally, at the start of the design process, the information on potential solutions needs to be generated quickly so that a designer can make effective decisions before moving on to detailed performance analysis, but the amount of information that can be obtained from simple analysis tools is limited. Machine learning may be able to assist by increasing the amount of information available at the early stages of the design process. This is not a new concept, in fact it has been considered for several decades but has always been limited by the computational power available. Recent advances in machine learning might allow for the creation of a more effective “sizing” stage of the design process, thus reducing the cost of generating a final design. The goal of this paper is to review some of the work in applying artificial intelligence to the design and analysis of electromagnetic devices and to discuss what might be possible by considering some examples of the use of machine learning in several tools used in conventional design, which have been considered over the past three decades.
Keywords: Electrical machine design, machine learning, key performance indicators, material properties, optimization
DOI: 10.3233/JAE-230104
Journal: International Journal of Applied Electromagnetics and Mechanics, vol. 73, no. 4, pp. 237-254, 2023
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl