用于短脉冲ADITYA托卡马克早期破裂预测的可解释统计特征工程
Interpretable statistical feature engineering for early disruption prediction in the short pulse ADITYA tokamak
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中文总结 AI 辅助
本研究针对短脉冲ADITYA托卡马克，开发了基于统计特征与随机森林分类器的可解释机器学习框架，实现了高准确率的早期破裂预测，为短脉冲托卡马克实时等离子体控制提供了实用方案。
中文摘要 AI 辅助
可靠的早期破裂预测对托卡马克的安全运行和实时控制至关重要。然而，基于机器学习的预测框架主要针对中长脉冲装置，对可用预警时间天生受限的短脉冲托卡马克关注相对有限。本研究开发了一种可解释机器学习框架，用于利用欧姆变压器电源负转换器激活前的初始等离子体演化信息，对ADITYA托卡马克的破裂进行特征工程和早期预测。从不同运行时间窗口的常规可用等离子体诊断数据中提取包含均值、方差、偏度、峰度和小波能量熵的统计描述符，采用基于决策树的特征选择方法识别具有物理意义的破裂前兆并降低特征维度。这些选定的特征被用于训练随机森林分类器。所提出的框架在不同分析窗口中实现了稳定的预测性能，0-35 ms和0-40 ms窗口的最大ROC-AUC为0.87。使用简化特征集获得了相当且在某些情况下有所提升的性能，表明选定的统计描述符保留了破裂预测所需的必要信息。所提出的方法为短脉冲托卡马克的实时破裂预测提供了可解释且计算高效的框架，并证实精心设计的统计描述符可有效替代原始时间序列输入用于早期破裂预测，从而为ADITYA及ADITYA-U等短脉冲托卡马克的实时等离子体控制提供了实用途径。
英文摘要
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.
发表机构
- Group in Computational Science and HPC, Dhirubhai Ambani University（计算科学与高性能计算组，迪鲁拜·安巴尼大学）
机构由 AI 辅助整理，请以论文原文为准。
