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Item type:Publication, Access status: without files , Developments in machine learning based modeling and design for electromagnetic compatibility(IEEE, 2025-12-24); ; ; ; 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.Publicationtype: Journal Article78 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Data-Efficient Prediction of the Specific Absorption Rate in a Human Head Model Exposed to a Plane EM Wave Using Gaussian Process Regression(2024-09); ; In this contribution, the specific absorption rate (SAR) in a human head model exposed to a 13.56 MHz external electromagnetic (EM) wave is predicted using Gaussian process regression (GPR). The electrical properties of the tissues are varied around the nominal values by ± 20% to account for material uncertainties. The GPR model achieves an R2 regression score higher than 0.99. The maximum and the worst-case exposure over the variation space are found using a tenth of the samples needed by random sampling on average. This allows the fast comparison against standards with a high degree of confidence. A sensitivity analysis of the exposure is extracted from the GPR model to provide an insight into the effects of the different parameter variations. The proposed framework is applicable to radiated susceptibility scenarios and beyond.Publicationtype: Conference Paper18