applied sciences Review Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions Yanbin Li 1, Gang Lei 2,* , Gerd Bramerdorfer 3 , Sheng Peng 1, Xiaodong Sun 4 and Jianguo Zhu 5 1 School of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 450007, China;
[email protected] (Y.L.);
[email protected] (S.P.) 2 School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia 3 Department of Electrical Drives and Power Electronics, Johannes Kepler University Linz, Linz 4040, Austria;
[email protected] 4 Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China;
[email protected] 5 School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW 2006, Australia;
[email protected] * Correspondence:
[email protected] Abstract: This paper reviews the recent developments of design optimization methods for electromag- netic 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 artifi- cial 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 Citation: Li, Y.; Lei, G.; reliability, several promising topics, including the application of cloud services and digital twin, are Bramerdorfer, G.; Peng, S.; Sun, X.; discussed as future directions for design optimization of electromagnetic devices.