A self-organised partition of the high dimensional plasma parameter space for plasma disruption prediction

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Enrico Aymerich, Alessandra Fanni, Fabio Pisano, Giuliana Sias, Barbara Cannas, JET Contributors and WPTE Team
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Abstract

This paper introduces a disruption predictor constructed through a fully unsupervised two-dimensional mapping of the high-dimensional JET operational space. The primary strength of this disruption predictor lies in its inherent self-organization capability. Diverging from both supervised disruption predictors and earlier approaches suggested by the same authors, which were based on unsupervised models such as Self-Organizing or Generative Topographic Maps, this predictor eliminates the need for labeling data of disruption terminated pulses during training. In prior methods, labels were indeed required post-mapping to inform the model about the presence or absence of disruption precursors at each time instant during the disrupted discharges. In contrast, our approach in this study involves no labeling of data from disruption-terminated experiments. The Self-Organizing Map, operating without any a priori information, adeptly identifies the regions characterizing the pre-disruptive phase. Moreover, SOM discovers non-trivial relationships and captures the complicated interplay of device diagnostics on the internal plasma states from the experimental data. The provided model is highly interpretable; it allows the visualization of high-dimensional data and facilitates easy interrogation of the model to understand the reasons behind its correlations. Hence, utilizing SOMs across various devices can prove invaluable in extracting rules and identifying common patterns, thereby facilitating extrapolation to ITER of the knowledge acquired from existing tokamaks.
用于等离子体破坏预测的高维等离子体参数空间自组织分区
本文介绍了通过对高维 JET 运行空间进行完全无监督的二维映射而构建的中断预测器。这种中断预测器的主要优势在于其固有的自组织能力。与监督中断预测器和同一作者早期提出的基于自组织或生成地形图等无监督模型的方法不同,该预测器在训练过程中无需对中断终止脉冲数据进行标记。在之前的方法中,确实需要在映射后使用标签来告知模型在中断放电期间的每个时间瞬间是否存在中断前兆。相比之下,我们在本研究中采用的方法无需对中断终止实验的数据进行标记。自组织图在没有任何先验信息的情况下,就能很好地识别出中断前阶段的特征区域。此外,自组织图还能发现非对称关系,并从实验数据中捕捉设备诊断对内部等离子状态的复杂相互作用。所提供的模型具有很强的可解释性;它允许高维数据的可视化,并便于对模型进行询问,以了解其相关性背后的原因。因此,在各种装置中使用 SOM 可以证明在提取规则和识别共同模式方面非常有价值,从而有助于将从现有托卡马克获得的知识推广到热核实验堆。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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