Interpretable statistical feature engineering for early disruption prediction in the short pulse ADITYA tokamak
Reliable early disruption prediction is critical for the safe operation and real-time control of tokamaks. However, machine learning based prediction frameworks have predominantly targeted medium and long pulse devices, with comparatively limited attention given to short pulse tokamaks where available warning time is inherently constrained. In this work, an interpretable machine learning framework is developed for feature engineering and early prediction of disruptions in the ADITYA using the initial plasma evolution information, prior to the activation of the negative converter of the ohmic transformer power supply. Statistical descriptors comprising the mean, variance, skewness, kurtosi...
| Authors | Jyoti Agarwal, Kavit Patel, Bhaskar Chaudhury et al. |
|---|---|
| Primary category | physics.plasm-ph |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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