Interpretable statistical feature engineering for early disruption prediction in the short pulse ADITYA tokamak

2026-08-23

AI Summary

本文针对短脉冲托卡马克装置的早期扰动预测问题，提出了一种基于统计特征工程和机器学习的方法，通过提取等离子体诊断数据中的统计描述符，并使用决策树进行特征选择及随机森林分类器训练，实现了稳定且高效的预测性能。

Abstract

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, kurtosis and wavelet energy entropy are extracted from routinely available plasma diagnostics over different operation time windows. Decision tree based feature selection is employed to identify physically meaningful disruption precursors and to reduce feature dimensionality. These selected features are used to train a random forest classifier. The proposed framework achieves stable predictive performance across different analysis windows, with a maximum ROC-AUC of 0.87 for 0-35 ms and 0-40 ms windows. Comparable and in some cases improved, performance is obtained using the reduced feature set, demonstrating that the selected statistical descriptors retain the essential information required for disruption prediction. The proposed methodology provides an interpretable and computationally efficient framework for real time disruption prediction in short pulse tokamaks and establishes that carefully engineered statistical descriptors can effectively replace raw time series inputs for early disruption prediction, thereby offering a practical pathway toward real time plasma control in short pulse tokamaks similar to ADITYA and ADITYA-U.

Problem

• short pulse tokamaks
• early disruption prediction
• real-time control

Innovation

• interpretable machine learning
• statistical feature engineering
• early disruption prediction
• short pulse tokamak
• real-time control