Dual-Driver Spectral Coherence as a Validation Framework for Synthetic-Real Systems

This proof-of-concept demonstrates a novel validation framework for assessing coherence between synthetic and real data systems. Both channels — a synthetic KPI simulation and a text-derived real-world signal — are simultaneously driven by the same external excitation (macro-index or PRBS). Spectral coherence, Bode gain/phase similarity, and information-theoretic metrics quantify their dynamic alignment. The method is data-agnostic, ontology-compatible, and reproducible through JSON-LD evidence artifacts. It establishes a generalizable criterion for validating synthetic-real realism across domains such as industry, economics, and complex adaptive systems. Keywords: spectral coherence, dual-driver validation, synthetic-real systems, Fourier analysis, ruahAI Forge, ontology resilience.