DDSNet：用于PCSEL属性预测的双域对称感知网络
DDSNet: Dual-domain Symmetry-aware Network for PCSEL Property Prediction
浏览论文内容
中文总结 AI 辅助
研究针对光子晶体表面发射激光器晶格设计，因耦合波理论计算成本高而需神经替代模型的问题，提出双域对称感知网络DDSNet，结合光谱滤波与结构先验，实验证明其在属性预测等方面优于现有基线，能有效捕捉结构-属性关系。
中文摘要 AI 辅助
高效探索光子晶体（PhC）晶格设计空间对开发光子晶体表面发射激光器至关重要。耦合波理论（CWT）虽提供有效物理框架，但大规模探索计算成本过高，催生对神经替代模型的需求。现有AI模型未充分利用CWT指出的PhC晶胞介电模式的光谱成分和不对称结构这两个关键因素，影响替代精度和筛选可靠性。为此提出双域对称感知网络（DDSNet），它将平移等变光谱滤波与对称诱导结构先验相结合。实验表明DDSNet在属性预测和高通量筛选方面显著优于现有AI基线，在结构敏感区域表现出卓越可靠性，有效捕捉物理上有意义的结构-属性关系，为PhC设计空间探索建立了高度可靠的神经替代模型。
英文摘要
Efficient exploration of the photonic crystal (PhC) lattice design space is essential for developing photonic crystal surface-emitting lasers. While coupled-wave theory (CWT) provides an effective physical framework, its computational cost remains prohibitive for large-scale exploration, driving the demand for neural surrogates. However, existing AI models underexploit two key factors of PhC unit-cell dielectric patterns indicated by CWT: spectral components and asymmetric structures, which largely govern devices' physical properties. This mismatch weakens surrogate accuracy and screening reliability, especially in structure-sensitive regions. To address this, we propose the Dual-Domain Symmetry-Aware Network (DDSNet). It integrates translation-equivariant spectral filtering with a symmetry-induced structural prior. The spectral filtering injects a spectral inductive bias into vision model while preserving translation equivariance on lattices. Meanwhile, the structural prior decomposes lattice features into irreducible representation-associated, symmetry-resolved components and processes them in separate branches. Experiments demonstrate that DDSNet significantly outperforms existing AI baselines in property prediction and high-throughput screening, exhibiting superior reliability in structure-sensitive regions. Crucially, component masking analyses reveal that the network successfully learns property-specific dependencies aligned with physical priors. These results indicate that DDSNet effectively captures physically meaningful structure-property relationships, establishing a highly reliable neural surrogate for PhC design space exploration.
发表机构
- Nanjing University（南京大学）
- University of Electronic Science and Technology of China（电子科技大学）
机构由 AI 辅助整理，请以论文原文为准。
