基于可微FDTD的带选微波腔优化：梯度引导搜索与结构化随机搜索
Band-Selective Microwave Cavity Optimization Using Differentiable FDTD: Gradient-Guided Search Versus Structured Random Search
AI总结：
该研究对比梯度基优化与结构化随机搜索在微波腔设计中的性能，基于自研JAX可微FDTD求解器，发现梯度优化在72次配对比较中均更优，平均提升0.356，支持其在该基准中的优势。
AI中文摘要：
我们使用自研的基于JAX的可微FDTD求解器，对比了梯度基逆设计与结构化随机搜索在介质加载微波腔中的应用。基准测试固定了材料占比、滤波密度表示、初始化、带能量目标及测量的选择墙时间，每个选定的设计在独立的8000步FDTD模拟中评估。在四个目标带、三个规定材料占比及六个随机种子的情况下，梯度优化在全部72次配对比较中实现了更高的带内光谱能量占比，平均配对提升为0.356，95%配对自助抽样区间为0.339-0.374，精确双侧符号翻转p值为0.03125。推理前通过了梯度、腔模、CFL、材料占比及原始输出完整性检查。在匹配的材料与计算预算下，结果支持梯度基优化在该腔设计基准中具有优势。
英文摘要：
We compare gradient-based inverse design with structured random search for dielectric-loaded microwave cavities using an in-house JAX-based differentiable FDTD solver. The benchmark fixes material fraction, filtered density representation, initialization, band-energy objective, and measured selection wall time. Each selected design is evaluated in a separate 8000-step FDTD simulation. Across four target bands, three prescribed material fractions, and six seeds, gradient optimization achieves a higher in-band spectral-energy fraction in all 72 paired comparisons. The mean paired improvement is 0.356, with a 95 percent paired bootstrap interval of 0.339-0.374 and an exact two-sided sign-flip p value of 0.03125. Gradient, cavity-mode, CFL, material-fraction, and raw-output integrity checks pass before inference. Under matched material and computational budgets, the results support an advantage for gradient-based optimization in this cavity-design benchmark.
