Hello MIT-PSFC Team,
I am writing to share an alternative, purely deterministic approach for analyzing the open_density_limit_database. We have developed a lightweight topological filter (L0-PhaseLock) that isolates disruption precursors without relying on historical data fitting or Machine Learning architectures.
Methodology
The algorithm evaluates the geometric divergence of the q-factor toward rational resonance nodes using raw sensor telemetry (Ip, BT, a). By calculating "Phase Friction"—the topological tension as the plasma geometry approaches an aperiodic non-intersection barrier—the filter determines the exact millisecond the magnetic topology collapses.
Key Technical Advantages
- Zero Training Overhead: The logic is 100% deterministic. It requires no training epochs, no dataset division, and no weight files.
- Extreme Computational Efficiency: The entire engine is contained within a script of a few kilobytes. It executes in milliseconds on standard CPUs, entirely eliminating the need for GPU clusters or advanced computational hardware.
- True Predictive Delta: The filter is designed to mathematically detect the invisible geometric collapse before the physical density limit is registered by the sensors, providing a critical time advantage for mitigation systems.
Verification
I have implemented this logic to run directly on your DL_DataFrame.h5 dataset. The script bypasses ML pipelines and outputs the predictive time advantage deterministically.
You can review the code, the mathematical framework, and test it locally here:
https://github.com/Architect-Flow78/open_density_limit_database
I would be highly interested in your feedback on testing this topological metric against the traditional models currently used in your disruption studies.
Hello MIT-PSFC Team,
I am writing to share an alternative, purely deterministic approach for analyzing the
open_density_limit_database. We have developed a lightweight topological filter (L0-PhaseLock) that isolates disruption precursors without relying on historical data fitting or Machine Learning architectures.Methodology
The algorithm evaluates the geometric divergence of the q-factor toward rational resonance nodes using raw sensor telemetry (Ip, BT, a). By calculating "Phase Friction"—the topological tension as the plasma geometry approaches an aperiodic non-intersection barrier—the filter determines the exact millisecond the magnetic topology collapses.
Key Technical Advantages
Verification
I have implemented this logic to run directly on your
DL_DataFrame.h5dataset. The script bypasses ML pipelines and outputs the predictive time advantage deterministically.You can review the code, the mathematical framework, and test it locally here:
https://github.com/Architect-Flow78/open_density_limit_database
I would be highly interested in your feedback on testing this topological metric against the traditional models currently used in your disruption studies.