Developments in machine learning based modeling and design for electromagnetic compatibility

Journal
IEEE electromagnetic compatibility magazine  
Volume
14
Issue
4
Start Page
69
End Page
80
Citation
IEEE Electromagnetic Compatibility Magazine 14 (4): 69-80 (2025)
Publisher DOI
10.1109/MEMC.2025.11314902
Scopus ID
2-s2.0-105026057720
Publisher
Institute of Electrical and Electronics Engineers
Machine learning (ML) has gained traction and emerged as one of most researched topics in recent years. Many engineering disciplines are taking advantage of a multitude of developed ML tools to enhance the engineering workflow, solve complicated problems or speedup design processes. This paper presents an overview on past and present developments of ML-based modeling and design in the field of electromagnetic compatibility (EMC) and the related fields of signal integrity (SI), power integrity (PI), bioelectromagnetics (BioEM) and electromagnetic field (EMF) scans. A critical analysis of recent research on ML applications is carried out to identify the arising opportunities and challenges facing the wide adoption of ML for the modeling and design in EMC. Based on current trends, a vision for ML-based modeling and design in EMC is presented in an attempt to foresee the direction of near-future research. A set of recommendations is formulated to tackle actual challenges facing a wider adoption. ML integration in EMC engineering has a great potential to revolutionize the modeling and design processes, despite the open challenges and the rising complexities of the faced problems.
Subjects
bioelectromagnetics (BioEM)
Data-Driven
electromagnetic compatibility (EMC)
electromagnetic field (EMF) scans
Machine Learning
power integrity (PI)
signal integrity (SI)
DDC Class
621.3: Electrical Engineering, Electronic Engineering