Pattern Inertia: An Information-Geometric Model of Biophysical Resistance to Coherence Realignment

This work introduces Pattern Inertia, a novel scalar field grounded in Fisher information geometry, to model a system’s resistance to coherence realignment across biological, psychological, and systemic domains. Defined as a function of local metric curvature and trajectory velocity in alignment space, $I(\theta)$ provides a mathematically precise, empirically estimable framework for understanding transformation dynamics. The concept is applicable to therapeutic resistance in oncology, adaptive thresholds in neuroplasticity, and nonlinear healing trajectories in psychosomatic contexts. The proposed metric integrates directly with the Universal Pattern Dynamics Equation (UPDE), adding a coherence-damping term that governs phase transitions in system alignment. This preprint includes the full mathematical formalism, clinical relevance, experimental validation strategies, and example Python pseudocode for simulation. It bridges theoretical physics, systems biology, and healing science through the unifying geometry of resistance to becoming.