In this paper, we present a new deep-learning disruption-prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruption data from the new devices. The explorative data analysis, conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the instantaneous plasma-state data, with further advantageous implications for a sequence-based predictor. Based on such important findings, we have designed a new algorithm for multi-machine disruption prediction that achieves high predictive accuracy for the C-Mod (AUC = 0.801), DIII-D (AUC = 0.947) and EAST (AUC = 0.973) tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that a boosted accuracy (AUC = 0.959) is achieved for the EAST predictions by including only 20 disruptive discharges with thousands of non-disruptive discharges from EAST in the training, combined with more than a thousand discharges from DIII-D and C-Mod. The improvement in the predictive ability obtained by combining disruption data from other devices is found to be true for all permutations of the three devices. Furthermore, by comparing the predictive performance of each individual numerical experiment, we find that non-disruption data are machine-specific, while disruption data from multiple devices contain device-independent knowledge that can be used to inform predictions for disruptions occurring in a new device.

在本文中，我们基于探索性数据分析的重要发现提出了一种新的深度学习中断预测算法，该算法有效地允许将知识从现有设备转移到新设备，从而使用来自新设备的非常有限的中断数据来预测中断。通过无监督聚类技术进行的探索性数据分析证实，与瞬时等离子体状态数据相比，时间序列数据是破坏性和非破坏性行为更好的分离器，对基于序列的预测器具有进一步的有利影响。基于这些重要发现，我们设计了一种用于多机中断预测的新算法，该算法对 C-Mod (AUC = 0.801)、DIII-D (AUC = 0.947) 和 EAST (AUC = 0. 973）具有有限超参数调整的托卡马克。通过数值实验，我们表明通过在训练中仅包含 20 次破坏性放电和来自 EAST 的数千次非破坏性放电，可以提高 EAST 预测的准确性（AUC = 0.959）， 结合 来自 DIII-D 和 C-Mod 的一千多次放电。发现通过组合来自其他设备的中断数据获得的预测能力的改进对于三个设备的所有排列都是正确的。此外，通过比较每个单独的数值实验的预测性能，我们发现非中断数据是特定于机器的，而来自多个设备的中断数据包含与设备无关的知识，可用于通知对新设备中发生的中断的预测.