用扩散模型直接生成满足特定散射需求的超表面结构，设计速度提升百倍。
Diffusion-Based Electromagnetic Inverse Design of Scattering Structured Media
- 基于条件扩散模型，从目标散射轮廓直接生成超表面几何
- 未见目标上中位数误差低于19%（最优仅1.39%），设计时间从小时级缩至秒级
- 适合快速探索复杂光子结构，加速新一代通信系统研发
我们提出一种用于电磁逆向设计的条件扩散模型，可直接从目标微分散射截面分布生成结构化介质几何形状，避免了耗时的迭代优化。采用带特征逐元素线性调制的1D U-Net架构，学习将期望的角向散射模式映射为2×2介电球结构，通过采样多样有效设计自然处理逆问题的非唯一性。在11,000个仿真超表面上训练后，模型在未见目标上的中位数相对误差（MPE）低于19%（最佳为1.39%），性能优于CMA-ES进化优化，同时将设计时间从数小时缩短至数秒。结果表明，扩散模型在电磁逆向设计中具有广阔前景，有望推动复杂超表面架构的快速探索，加速下一代光子与无线通信系统的发展。代码已公开于https://github.com/mikzuker/inverse_design_metasurface_generation。
原文摘要 · Abstract (English)
We present a conditional diffusion model for electromagnetic inverse design that generates structured media geometries directly from target differential scattering cross-section profiles, bypassing expensive iterative optimization. Our 1D U-Net architecture with Feature-wise Linear Modulation learns to map desired angular scattering patterns to 2x2 dielectric sphere structure, naturally handling the non-uniqueness of inverse problems by sampling diverse valid designs. Trained on 11,000 simulated metasurfaces, the model achieves median MPE below 19% on unseen targets (best: 1.39%), outperforming CMA-ES evolutionary optimization while reducing design time from hours to seconds. These results demonstrate that employing diffusion models is promising for advancing electromagnetic inverse design research, potentially enabling rapid exploration of complex metasurface architectures and accelerating the development of next-generation photonic and wireless communication systems. The code is publicly available at https://github.com/mikzuker/inverse_design_metasurface_generation.
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