Same framework, applied to Fusion
The framework's value lies in its universality across disparate domains. The brake operator \(\mathcal{B}\), dispersion \(\mathcal{S}\), consensus \(\mathcal{M}\), spectral primitive \(\mathcal{P}\), anti-shadow detector \(\mathfrak{A}\), and scope-reporter \(\mathscr{A}\) — together with Theorems 1–13 — are applied here exactly as on every other domain. Source code: github.com/senuamedia/uniformity. No per-domain calibration. No imported threshold. No bespoke fit.
Cross-domain catalogue — \(\beta\)-strip
This domain has no catalogue instance reported yet. The chart below shows the framework's brake-exponent readings across every other domain — the same operators apply unchanged when data are added here.
Click any point for the full reading: instance, domain, \(\beta\) value, and a link to the source code.
What the framework provides for fusion plasma
Fusion plasma research has rich diagnostic suites and machine-specific predictors (DECAF, Kates-Harbeck deep-learning predictor, Rea et al. 2023). These methods are highly tuned to specific machines, limiting transfer between DIII-D and JET, between L-mode and H-mode, and between operational and ITER-scale plasmas. The framework's planned contribution is machine-agnostic disruption precursor detection via cross-diagnostic consensus on brake-exponent evolution.
Status
Open direction
No strong catalogue instance yet. domains/fusion/ — experiments directory empty; planned-experiment specifications in README. No catalogue instance reported. Open direction; the framework primitives apply unchanged when data are added.
Open applications
- DIII-D disruption cross-diagnostic consensus — \(\mathcal{M}\) across {Te, ne, Mirnov, ECE, soft X-ray}.
- JET cross-machine universality — do DIII-D and JET share a universal \(\beta\) evolution before disruption?
- L–H transition — brake operator on the H-mode transition as softer regime change.
- Confinement scaling cross-shadow — IPB98(y,2) vs ITER89P as independent shadows.
Framework reading
Cross-shadow consensus directly embodies Law III: a real precursor is one multiple diagnostics see; a single-diagnostic spike is suspect. The framework should generalise where machine-specific deep-learning predictors fail to transfer.