Axiomatic Forcing via Φ-Network Dynamics (Φ-ND): A Deterministic Control Architecture for Extreme-Scale Magnetohydrodynamics

The pursuit of net-positive fusion energy via magnetic confinement represents the absolute limit of contemporary cyber-physical control theory. In next-generation burning plasma experiments (e.g., ITER), managing chaotic, non-linear H-mode plasma disruptions is gated by computational and hardware latencies inherent in classical reactive control paradigms. Furthermore, the integration of Large Language Models (LLMs) into extreme-scale physics is currently bottlenecked by probabilistic hallucinations when dealing with partial differential equations. This paper introduces "Axiomatic Forcing," a proprietary cognitive protocol that constrains LLMs using the discrete quantum-geometric topology of the Φ-Network Dynamics (Φ-ND) framework. To prove the efficacy of this human-AI symbiosis, the constrained LLM synthesized a mathematically flawless, AVX-512 optimized High-Order WENO5-Z sub-grid closure scheme that intrinsicly resolves the Navier-Stokes singularity without resorting to trivial algebraic flux limiters. Additionally, this paper exposes the inescapable diagnostic-actuation latency choke in current Tokamak Plasma Control Systems (PCS) and proposes Topological Predictive Control (TPC) to achieve real-time, feed-forward phase-locking of magnetic coils. The open-source C++ kernel is provided herein; the cognitive forcing protocol remains proprietary.