Band-Selective Microwave Cavity Optimization Using Differentiable FDTD: Gradient-Guided Search Versus Structured Random Search

Abstract

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.