Abstract In this paper we lay the groundwork for a robust cross-device comparison of data-driven disruption prediction algorithms on DIII-D and JET tokamaks. In order to consistently carry on a comparative analysis, we define physics-based indicators of disruption precursors based on temperature, density, and radiation profiles that are currently not used in many other machine learning predictors for DIII-D data. These profile-based indicators are shown to well-describe impurity accumulation events in both DIII-D and JET discharges that eventually disrupt. The univariate analysis of the features used as input signals in the data-driven algorithms applied on the data of both tokamaks statistically highlights the differences in the dominant disruption precursors. JET with its ITER-like wall is more prone to impurity accumulation events, while DIII-D is more subject to edge-cooling mechanisms that destabilize dangerous magnetohydrodynamic modes. Even though the analyzed data sets are characterized by such intrinsic differences, we show through a few examples that the inclusion of physics-based disruption markers in data-driven algorithms is a promising path toward the realization of a uniform framework to predict and interpret disruptive scenarios across different tokamaks. As long as the destabilizing precursors are diagnosed in a device-independent way, the knowledge that data-driven algorithms learn on one device can be re-used to explain a disruptive behavior on another device.

中文翻译：

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跨托卡马克可解释的基于机器学习的中断预测器的进展

摘要 在本文中，我们为 DIII-D 和 JET 托卡马克上数据驱动的中断预测算法的稳健跨设备比较奠定了基础。为了始终如一地进行比较分析，我们根据温度、密度和辐射分布定义了基于物理的破坏前兆指标，这些指标目前未用于 DIII-D 数据的许多其他机器学习预测器。这些基于剖面的指标可以很好地描述 DIII-D 和 JET 排放中最终破坏的杂质积累事件。在应用于两个托卡马克数据的数据驱动算法中用作输入信号的特征的单变量分析在统计上突出了主要破坏前兆的差异。具有类似 ITER 壁的 JET 更容易发生杂质积累事件，而 DIII-D 更容易受到边缘冷却机制的影响，这些机制会破坏危险的磁流体动力学模式。尽管分析的数据集具有这种内在差异的特征，但我们通过一些示例表明，在数据驱动算法中包含基于物理的破坏标记是实现统一框架以预测和解释破坏性场景的有希望的途径跨越不同的托卡马克。只要以独立于设备的方式诊断不稳定的前兆，数据驱动算法在一台设备上学习的知识就可以重新用于解释另一台设备上的破坏性行为。我们通过几个例子表明，在数据驱动算法中包含基于物理的破坏标记是实现统一框架以预测和解释不同托卡马克的破坏场景的有希望的途径。只要以独立于设备的方式诊断不稳定的前兆，数据驱动算法在一台设备上学习的知识就可以重新用于解释另一台设备上的破坏性行为。我们通过几个例子表明，在数据驱动算法中包含基于物理的破坏标记是实现统一框架以预测和解释不同托卡马克的破坏场景的有希望的途径。只要以独立于设备的方式诊断不稳定的前兆，数据驱动算法在一台设备上学习的知识就可以重新用于解释另一台设备上的破坏性行为。