## 2.2. _Applied sciences_  
### 2.2. _Eiditorial_  
Data-Driven Methods for Design and Analysis of Electromagnetic Devices  
**[PERSON] 1,*+**, [PERSON] 1,** [PERSON] 2 and [PERSON] 3  
Footnote 1: School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia; [EMAIL_ADDRESS]  
Footnote 2: School of Electrical and Information Engineering, The University of Sydney, Camperdown, NSW 2006, Australia; [EMAIL_ADDRESS]  
Footnote 3: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China; [EMAIL_ADDRESS]  
Footnote 4: Correspondence: [EMAIL_ADDRESS]  
Electromagnetic devices, such as electrical drive systems for electric vehicles, have been widely employed in domestic and industrial equipment. In the context of Industry 4.0, their analysis methods are experiencing significant progress and innovation due to integrating industrial big data. Researchers have explored data-driven approaches for various electromagnetic devices and systems to facilitate this progress. These include modelling, design, optimisation, condition monitoring, and reliability and robustness evaluation. One such approach is the data-driven design optimisation method in permanent magnet motors for electric vehicles. Additionally, data-driven fault diagnosis methods have been developed for wind power generators. These new models and analysis methods will benefit the development of digital twins for electromagnetic devices. This Special Issue aims to present a collection of scientific manuscripts covering the theoretical and practical aspects associated with data-driven methods for designing and analysing electromagnetic devices and systems. A total of ten research articles are presented. [PERSON] et al. [1] proposed a physics-informed generative adversarial network (PIGAN) and a permanent magnet linear synchronous motor (PMLSM)-based magnetic field dataset for fast magnetic field estimation. In the training process, physics-informed loss functions were utilised, resulting in the output governed by Maxwell's equation. Different slot-pole combinations of the PMLSM were involved in the dataset to extend the generalisation of PIGAN. The effectiveness of the physics-informed method was verified by comparing the magnetic field approximation results and the performance analysis results of the PMLSM with those of the finite element method (FEM), and the speed of PIGAN is approximately 40 times faster than that of FEM while maintaining similar accuracy. [PERSON] et al. [2] introduced geometric parametric models to evaluate the multi-physical performances of electrical machines and built a machine-learning model that can predict the multi-physical characteristics of electrical machines from input geometrical parameters. Three individual machine-learning models were built based on the data obtained from 2D/3D motor parametric models: a model for the torque and back electromotive force harmonic orders, a model for motor losses, and a model for natural frequencies of the mode shapes. Finally, the developed multi-attribute model was integrated into an optimisation routine to compare the computational time with the classical finite element analysis (FEA) optimisation approach. [PERSON] et al. [3] investigated the effect of the alternate flux bridge combining Halbach array in permanent magnet vernier machines. It is found that the new design can provide a flux path for low-order harmonics and effectively improve the torque density of the machine. Through FEA and experimental verification, the torque density of the proposed design reaches 40.11 K\(\text{N}\text{m}/\text{m}^{3}\), significantly higher than its existing counterparts. [PERSON] et al. [4] proposed a global sensitivity analysis procedure (based on the Monte Carlo method) that identifies the most influential design parameters and determines guidelines to reduce the vibration of permanent magnet synchronous motors. The proposed sensitivity analysis method was performed for a particular electric motor design.