A predictor model of radiative collapse of stellarator-heliotron plasmas has been developed by means of a machine learning technique and the feature of radiative collapse has been extracted with sparse modeling. The dataset used for training the model is constructed based on density ramp-up experiments in the Large Helical Device. As a result of feature extraction, the line averaged electron density, visible line emissions of CIV and OV, and the electron temperature at the edge have been selected as key parameters of radiative collapse. The likelihood of occurrence of radiative collapse has been quantified by using these parameters and this likelihood has been assessed in terms of predicting capability of the occurrence of radiative collapse. The collapse likelihood also implies the underlying physics of radiative collapse, therefore, the knowledge obtained by this data-driven study is expected to facilitate elucidation of the physics of the radiative collapse. In validation with discharges outside of the dataset, the predictor based on the likelihood has predicted over 85% of radiative collapse about 100 ms prior to this event on average while about 5% of stable discharges have been detected falsely as collapse discharges. The discharges in which the predictor made faults are discussed in order to investigate the cause of failure.

中文翻译：

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使用穷举搜索的特征提取预测大型螺旋装置中的辐射坍塌

利用机器学习技术建立了仿星器-日光加速器等离子体辐射坍缩预测模型，并通过稀疏建模提取了辐射坍缩特征。用于训练模型的数据集是基于大型螺旋装置中的密度提升实验构建的。作为特征提取的结果，线平均电子密度、CIV和OV的可见线发射以及边缘处的电子温度被选为辐射坍缩的关键参数。已经通过使用这些参数量化了辐射坍塌发生的可能性，并且已经根据预测辐射坍塌发生的能力来评估这种可能性。坍塌可能性也暗示了辐射坍塌的基础物理学，因此，预计通过这项数据驱动研究获得的知识将有助于阐明辐射坍缩的物理学。在对数据集外的放电进行验证时，基于可能性的预测器平均在此事件发生前约 100 毫秒预测了超过 85% 的辐射坍塌，而大约 5% 的稳定放电被错误地检测为坍塌放电。讨论了预测器发生故障的放电，以调查故障原因。基于可能性的预测器平均在此事件发生前大约 100 毫秒预测了超过 85% 的辐射坍塌，而大约 5% 的稳定放电被错误地检测为坍塌放电。讨论了预测器发生故障的放电，以调查故障原因。基于可能性的预测器平均在此事件发生前大约 100 毫秒预测了超过 85% 的辐射坍塌，而大约 5% 的稳定放电被错误地检测为坍塌放电。讨论了预测器发生故障的放电，以调查故障原因。