High-Asymmetry Metasurface: A New Solution for Terahertz Resonance via Active Learning-Augmented Diffusion Model.

ABSTRACT: Terahertz (THz) metamaterials with high-figure-of-merit (high-FoM) performance resonance are essential for advancing sensors, detectors, and imagers. Conventional designs focus on symmetric or low-asymmetry geometric structures, leaving high-asymmetry designs largely unexplored due to the inefficiency of trial-and-error-based rational design. Recent deep learning techniques offer automation and acceleration but are constrained by the need for large datasets inherent to their data-driven nature. Here, a novel prior knowledge-guided generative model augmented by a physics-constrained active learning mechanism to design high-asymmetry metamaterials. An advanced diffusion model learns features from a small set of classical structures with high-FoM THz resonance and generates new high-asymmetry

SUBMITTER: Dai Q 

PROVIDER: S-EPMC12786302 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature