ML-inspired Luneburg Lens Design

Designing the properties of different constant index shells in Luneburg lenses is challenging due to the mathematical complexity involved. While numerous methods have been developed to address this issue, they often prove to be either too complicated or time-consuming. However, recent advancements in machine learning (ML) have accelerated solutions to many such engineering problems. In this work, we demonstrate how ML can simplify the design of multishell Luneburg lenses, significantly reducing the effort and computational resources required. Specifically, we employ k-means clustering to calculate the properties of the lens’s various shells. Furthermore, we validate the effectiveness and robustness of our method by comparing our simulated results with those from previous studies.