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arXiv2026

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

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.

eess.SP↗

arXiv2025

AI-Hybrid TRNG: Kernel-Based Deep Learning for Near-Uniform Entropy Harvesting from Physical Noise

AI-Hybrid TRNG is a deep-learning framework that extracts near-uniform entropy directly from physical noise, eliminating the need for bulky quantum devices or expensive laboratory-grade RF receivers. Instead, it relies on a low-cost, thumb-sized RF front end, plus CPU-timing jitter, for training, and then emits 32-bit high-entropy streams without any quantization step. Unlike deterministic or trained artificial intelligence random number generators (RNGs), our dynamic inner-outer network couples adaptive natural sources and reseeding, yielding truly unpredictable and autonomous sequences. Generated numbers pass the NIST SP 800-22 battery better than a CPU-based method. It also passes nineteen bespoke statistical tests for both bit- and integer-level analysis. All results satisfy cryptographic standards, while forward and backward prediction experiments reveal no exploitable biases. The model's footprint is below 0.5 MB, making it deployable on MCUs and FPGA soft cores, as well as suitable for other resource-constrained platforms. By detaching randomness quality from dedicated hardware, AI-Hybrid TRNG broadens the reach of high-integrity random number generators across secure systems, cryptographic protocols, embedded and edge devices, stochastic simulations, and server applications that need randomness.

cs.CR↗