Disruptions are abrupt collapses of the configuration that have afflicted all tokamaks ever operated. Reliable observers are a prerequisite to the definition and the deployment of any realistic strategy of countermeasures to avoid or mitigate disruptions. Lacking first principle models of the dynamics leading to disruptions, in the past decades empirical predictors have been extensively studied and some were even installed in JET real time network. Having been conceived as engineering tools, they were often very abstract. In this work, physics and data-driven methodologies are combined to identify the main macroscopic precursors of disruptions: magnetic instabilities, abnormal kinetic profiles and radiation patterns. Machine learning predictors utilising these observers can not only detect and classify these anomalies but also determine their probability of occurrence and estimate the time remaining before their onset. These tools have been applied to a database of about two thousand JET discharges with various isotopic compositions including DT, in conditions simulating in all respects real time deployment. Their performance would meet ITER requirements, and they are expected to be easily transferrable to larger devices, because they rely only on normalised quantities, form factors, and physical/empirical scaling laws.

中文翻译：

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用于检测、分类和预测托卡马克等离子体中的异常和中断的混合物理/数据驱动逻辑

干扰是结构的突然崩溃，困扰着所有曾经运行过的托卡马克装置。可靠的观察员是定义和部署任何现实的对策以避免或减轻干扰战略的先决条件。由于缺乏导致破坏的动力学第一原理模型，在过去的几十年中，经验预测器已被广泛研究，有些甚至安装在 JET 实时网络中。它们被视为工程工具，通常非常抽象。在这项工作中，物理学和数据驱动的方法相结合，以确定破坏的主要宏观前兆：磁不稳定性、异常动力学曲线和辐射模式。利用这些观察者的机器学习预测器不仅可以检测和分类这些异常，还可以确定它们发生的概率并估计它们发生之前的剩余时间。这些工具已应用于约两千次 JET 放电的数据库，其中包含 DT 等各种同位素成分，在各方面模拟实时部署的条件。它们的性能将满足 ITER 要求，并且预计可以轻松转移到更大的设备，因为它们仅依赖于标准化数量、形状因素和物理/经验缩放定律。