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Item type:Publication, Access status: with files , Development of data-efficient machine learning approaches for the modeling and design in electromagnetic compatibility and radio-frequency engineering(2026); This thesis explores the use of machine learning (ML) in electromagnetic compatibility (EMC) engineering and related fields, focusing on common challenges such as model interpretability, high-dimensional design spaces, and data acquisition costs. It investigates ML methods that incorporate physical insights to improve modeling accuracy and reliability. Strategies to enhance data-efficiency and promote dataset reuse are proposed to support reproducibility and collaboration. Recommendations for future research and community efforts are provided to advance ML adoption in EMC and related fields. The results demonstrate the potential of ML to accelerate the design and modeling processes in EMC.Publicationtype: Doctoral Thesis31 17