基于等离子体断层成像的深度学习破坏前兆分析
Deep Learning for the Analysis of Disruption Precursors based on Plasma Tomography
摘要：
JET基线方案正在开发中，以实现高聚变性能和持续的聚变功率。然而，随着更高的等离子体电流和更高的输入功率，正在观察到脉冲破坏性的增加。虽然有各种各样的可能的破坏原因，目前的破坏似乎是密切相关的辐射现象，如杂质积累，核心辐射，辐射崩溃。在这项工作中，我们专注于测辐射热断层扫描重建等离子体辐射分布，并在它的顶部，我们应用异常检测，以确定之前的辐射模式，重大中断。该方法广泛使用机器学习。首先，我们训练一个代理模型的等离子体层析成像的基础上矩阵乘法，这提供了一个快速的方法来计算整个范围内的任何给定的脉冲的等离子体辐射轮廓。然后，我们训练变分自动编码器，通过将它们编码成潜在分布并随后解码来再现辐射分布。作为一个异常检测器，变分自动编码器努力再现不寻常的行为，这不仅包括实际的中断，但他们的前兆以及。这些前兆是根据JET最近两次活动中所有基线脉冲的异常分数分析确定的。
英文摘要：
The JET baseline scenario is being developed to achieve high fusion performance and sustained fusion power. However, with higher plasma current and higher input power, an increase in pulse disruptivity is being observed. Although there is a wide range of possible disruption causes, the present disruptions seem to be closely related to radiative phenomena such as impurity accumulation, core radiation, and radiative collapse. In this work, we focus on bolometer tomography to reconstruct the plasma radiation profile and, on top of it, we apply anomaly detection to identify the radiation patterns that precede major disruptions. The approach makes extensive use of machine learning. First, we train a surrogate model for plasma tomography based on matrix multiplication, which provides a fast method to compute the plasma radiation profiles across the full extent of any given pulse. Then, we train a variational autoencoder to reproduce the radiation profiles by encoding them into a latent distribution and subsequently decoding them. As an anomaly detector, the variational autoencoder struggles to reproduce unusual behaviors, which includes not only the actual disruptions but their precursors as well. These precursors are identified based on an analysis of the anomaly score across all baseline pulses in two recent campaigns at JET.
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