Inverse Design of Electromagnetic Devices via Learning-Assisted Approaches

Inverse Design (ID) has become a powerful tool in engineering, particularly within the inverse scattering (IS) framework. This paper explores the integration of IS-based design methodologies with deep learning (DL) to enhance efficiency, adaptability, and automation in electromagnetic (EM) device design. This framework ensures a fast and reliable ID procedure, making it well-suited for complex and non-conventional EM topologies.