AI-Assisted Optimization Framework for Advanced EM Problems

This thesis concerns the study, development and analysis of innovative artificial intelligence (AI)-driven optimization techniques within the System-by-Design (SbD) framework aimed at efficiently addressing the computational complexity inherent in advanced electromagnetic (EM) problems. By leveraging the available a-priori information as well as the proper integration of machine learning (ML) techniques with intelligent exploration strategies, the SbD paradigm enables the effective and reliable solution of the EM problem at hand, with user-defined performance and in a reasonable amount of time. The flexibility of the AI-driven SbD framework is demonstrated in practice with the implementation of two solution strategies to address the fully non-linear inverse scattering problem (ISP) for the detection and imaging of buried objects in ground penetrating radar (GPR)-based applications, and to address the design and optimization of mm-wave automotive radars that comply multiple challenging and contrasting requirements. A comprehensive set of numerical experiments is reported to demonstrate the efficacy and computational efficiency of the SbD-based optimization techniques in solving complex EM problems.