ΔΦ KERNEL GEOMETRY v2.0 — Redefining the Gaussian Kernel as Electrostatic Field Coherence

This paper redefines the Gaussian kernel through the Mitchell Equation (ΔΦ = ρ × v), transforming it from a spatial similarity measure into an electrostatic coherence function. The new formulation, K(x, x′) = exp(−ΔΦ(x, x′)² / 2σ²), replaces isotropic distance with directional tension resolution, embedding causality, asymmetry, and dynamic history directly into the learning process. This model is predictive for EEG coherence, biological memory collapse, and anisotropic machine learning tasks.