This paper reviews the recent developments of design optimization methods for
electromagnetic devices, with a focus on machine learning methods. First, the recent …

Electric motors find widespread application across various industrial fields. The pursuit of
enhanced comprehensive electric motors performance has consistently drawn significant …

Employing machine learning techniques in predicting the parameters of metamaterial
antennas has a significant impact on the reduction of the time needed to design an antenna …

This paper proposes a new method for accurately predicting rotating machine properties
using a deep neural network (DNN). In this method, the magnetic field distribution over a …

This review article systematically summarizes the existing literature on utilizing machine
learning (ML) techniques for the control and monitoring of electric machine drives. It is …

Electric motor is one of the core components of electronic propulsion systems and plays an
essential role in the industry. The optimal design of an electric motor poses a complex …

Efficiency map** has an important place in examining the maximum efficiency distribution
as well as the energy consumption of designed electric motors at maximum torque and …

The design of an electrical machine can be quantified and evaluated by Key Performance
Indicators (KPIs) such as maximum torque, critical field strength, costs of active parts, sound …

The use of behavioral models based on deep learning (DL) to accelerate electromagnetic
field computations has recently been proposed to solve complex electromagnetic problems …

Purpose The purpose of this paper is to develop surrogate models, using deep learning
(DL), that can facilitate the application of EM analysis software. In the current status quo …