基于扩散模型的介质谐振器超表面逆设计用于构建智能电磁环境
Diffusion-Based Inverse Design of Dielectric Resonator Metasurfaces for Shaping Smart Electromagnetic Environments
AI总结：
该研究提出条件扩散框架用于介质谐振器超表面逆设计，可生成多个候选设计，其误差低于CMA-ES优化及确定性神经基线，推理效率更高。
AI中文摘要：
未来无线系统有望将周围空间从被动传播介质转变为智能电磁环境，在该环境中，工程化表面可控制波传播、支持无线传感并生成可编程电磁指纹。实现这一愿景的关键挑战是针对定制化电磁传播的超表面逆设计。正向分析用于评估已知几何结构的响应，而逆任务则从指定的散射特征出发，寻求能产生该特征的物理可实现结构。该逆任务本质上是非线性且通常是高维的，候选解可能不唯一且无法直接指示实际可实现性。在此，我们引入一种条件扩散（conditional diffusion）框架，用于从目标角散射模式出发逆设计介质谐振器超表面。该模型在T矩阵模拟的几何结构-响应对上进行训练，学习几何结构的条件分布而非确定性映射，从而为该不适定逆问题提供多个候选设计。生成的最佳超表面达到1.39%的平均百分比误差，优于CMA-ES优化方法（10小时后误差为4.1%），且训练后推理仅需约1分钟。对于分布外光谱，该模型还产生比确定性神经基线更低的误差分布，凸显了扩散模型在高效超表面设计中的潜力。
英文摘要：
Future wireless systems are expected to transform the surrounding space from a passive propagation medium into a smart electromagnetic environment, where engineered surfaces control wave propagation, support wireless sensing, and create programmable electromagnetic fingerprints. A key challenge in realizing this vision is the inverse design of metasurfaces for tailored electromagnetic propagation. While forward analysis evaluates the response of a known geometry, the inverse task starts from a prescribed scattering signature and seeks a physically realizable structure that produces it. This inverse task is inherently nonlinear and often high-dimensional, while candidate solutions may be non-unique and provide no direct indication of practical realizability. Here, we introduce a conditional diffusion framework for inverse design of dielectric resonator metasurfaces from target angular scattering patterns. Trained on T-matrix simulated geometry-response pairs, the model learns a conditional distribution of geometries instead of a deterministic mapping, enabling multiple candidate designs for the ill-posed inverse problem. The best generated metasurface achieves a mean percentage error of 1.39%, outperforming CMA-ES optimization (4.1% after 10 h) while requiring only about one minute for after-training inference. The model also produces lower error distributions than deterministic neural baselines for out-of-distribution spectra, highlighting the potential of diffusion models for efficient metasurface design.
