Energetic beams excite semi-repetitive modes ('staircase mode') in the field-reversed configuration (FRC) plasma. We explore several neural network architectures to detect, and in some cases predict, this type of mode onset. We weigh the performance of these architectures and find that recurrent neural networks (RNNs), specifically long short-term memory (LSTM) networks, outperform all other models we examine. LSTMs can predict the onset of staircase with a lead window of 0.2 ms, which has implications for plasma longevity and is a promising direction for similar analysis in FRC devices in the future.

中文翻译：

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用循环神经网络检测和预测场反转配置等离子体中的光束驱动模式

高能光束在场反转配置 (FRC) 等离子体中激发半重复模式（“阶梯模式”）。我们探索了几种神经网络架构来检测并在某些情况下预测这种类型的模式开始。我们权衡了这些架构的性能，发现循环神经网络 (RNN)，特别是长短期记忆 (LSTM) 网络，优于我们检查的所有其他模型。LSTM 可以以 0.2 ms 的引导窗口预测阶梯的开始，这对等离子体寿命有影响，并且是未来 FRC 设备中类似分析的一个有希望的方向。