3D Printed Waveguide

3D-printed waveguides are revolutionizing various fields by enabling the creation of complex, customized wave-guiding structures with unprecedented design freedom. Current research focuses on optimizing waveguide design using machine learning techniques, such as Gaussian processes and neural networks, to predict and improve performance characteristics like bandwidth, non-reciprocity, and source localization accuracy. These advancements are impacting diverse applications, including acoustic sensing, antenna design, and photonic devices, by offering improved efficiency, miniaturization, and functionality. The integration of machine learning is accelerating the design process and enabling the exploration of novel waveguide topologies and functionalities.