Physics-driven unsupervised deep learning network for programmable metasurface-based beamforming.

ABSTRACT: Programmable metasurfaces have garnered significant attention for their capacity to dynamically manipulate electromagnetic (EM) waves. In particular, the programmable metasurfaces offer to generate a wide range of EM beams when the appropriate digital coding patterns are designed. Traditionally, optimizing the coding patterns involves time-consuming nonlinear optimization algorithms due to the high computational complexity. In this study, we propose a physics-assisted deep learning (DL) model that can calculate the coding pattern in milliseconds, requiring only a simple depiction of the desired beam. An extended version of the macroscopic model for digital coding metasurface is introduced as the physics-driven component, which can compute the radiation pattern rapidly based on the provided

SUBMITTER: Bao J 

PROVIDER: S-EPMC11379667 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature