Development of data-efficient machine learning approaches for the modeling and design in electromagnetic compatibility and radio-frequency engineering

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.

DDC Class

621.3: Electrical Engineering, Electronic Engineering

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

Youcef_Hassab_Development-of-Data-Efficient-Machine-Learning-Approaches-for-the-Modeling-and-Design-in-Electromagnetic-Compatibility-and-Radio-Frequency-Engineering.pdf

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27.29 MB

Format

Adobe PDF

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Development of data-efficient machine learning approaches for the modeling and design in electromagnetic compatibility and radio-frequency engineering