Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions
Source abstract
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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Research context
OpenAlex topics: Electric Motor Design and Analysis · Induction Heating and Inverter Technology · Advanced Welding Techniques Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 450007, China
School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Department of Electrical Drives and Power Electronics, Johannes Kepler University Linz, Linz 4040, Austria
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW 2006, Australia
Funding
National Natural Science Foundation of China · Education Department of Henan Province
References (100+)
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