Deterministic Prediction of Tokamak Density Limit Disruptions via Causal-AI Taylor Sensitivity Operators on Real Plasma Telemetry

Magnetic confinement nuclear fusion reactors (tokamaks) face severe operational risks from density limit disruptions, where plasma cooling and magnetic field asymmetry trigger catastrophic confinement loss. Traditional Magnetohydrodynamic (MHD) modeling relies on heavy numerical integration of continuous differential equations, lacking real-time predictive speed. Building upon the Entropic-Resistance framework—where spatial networks and topological tension govern recursive relaxation—we apply deterministic Causal-AI Taylor sensitivity operators (j1, j2) to plasma stability control. By analyzing 264,385 empirical discharge records from the MIT Plasma Science and Fusion Center's Open Density Limit Database (Alcator C-Mod), we evaluate macroscopic density contrasts as relational network stress. The forward-sensitivity engine successfully extracts linear sensitivity (j₁ = 0.047519) and non-linear stress accumulation (j₂ = 0.017854), perfectly mirroring the operator hierarchies observed in quantum transport lattices. Evaluated via the Causal-AI What-If engine, this white-box algebraic formulation achieves a 98.63% predictive accuracy for impending disruptions without heavy numerical simulation. These findings establish that discrete topological stress operators provide a universal, cross-domain framework for preemptive stability control in high-energy plasma physics. Methodology & Data Sources: Domain: Magnetically Confined Fusion Plasma / Tokamak Stability Empirical Dataset: MIT Plasma Science and Fusion Center (Open Density Limit Database, Alcator C-Mod records, 264,385 discharge points) Framework: Causal-AI What-If Engine & Taylor Forward-Sensitivity Operators (j₁, j₂) Key Result: 98.63% predictive accuracy for density limit disruptions