托卡马克等离子体诊断模型中的传感器鲁棒性基准测试：对托卡马克的系统评估
Benchmarking Sensor Robustness in Plasma Diagnostic Models: A Systematic Evaluation on TokaMark
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中文总结 AI 辅助
研究针对托卡马克等离子体诊断模型，利用TokaMark数据集在多种故障场景和插补策略下评估多种模型，引入鲁棒性分数，发现临近破裂传感器故障对序列模型影响大，统计模型较稳定，还得出等离子体电流是关键诊断指标等结论。
中文摘要 AI 辅助
托卡马克聚变装置的等离子体诊断模型几乎都是基于干净、完整的传感器数据进行评估。但实际中，聚变诊断经常失败，如采集系统启动延迟、单个传感器损坏以及在等离子体破裂临近时信号丢失聚集。本文利用11573个MAST shots的TokaMark数据集，针对等离子体诊断机器学习提出首个系统鲁棒性基准测试，在六种基于物理的故障场景和三种插补策略下评估XGBoost、LSTM、Transformer和TokaMark CNN基线。引入鲁棒性分数（RS）进行标准化跨架构比较。核心发现是临近破裂的传感器故障会使序列模型性能崩溃，而统计特征模型相对稳定。前向填充插补可消除序列模型随机丢失带来的几乎所有性能下降，但窗口末尾损坏时帮助不大。利用真实破裂时间戳进行的逐次警报评估显示，临近传感器故障时LSTM警报检测率降至TPR = 0.00，而均值填充插补可将其恢复到TPR = 1.00。等离子体电流是所有架构中最关键的诊断指标。代码、数据和训练好的检查点可在指定网址获取。
英文摘要
Plasma diagnostic models for tokamak fusion devices are almost universally evaluated on clean, complete sensor data. In practice, fusion diagnostics fail regularly: acquisition systems start late, individual sensors die, and signal dropouts cluster precisely when a plasma disruption is approaching. We present the first systematic robustness benchmark for plasma diagnostic ML using the TokaMark dataset of 11,573 MAST shots, evaluating XGBoost, LSTM, Transformer, and the TokaMark CNN baseline across six physically-grounded failure scenarios and three imputation strategies. We introduce the Robustness Score (RS) for standardized cross-architecture comparison. Our central finding is that disruption-proximate sensor failure (corruption injected in the final window timesteps) collapses sequence model performance (LSTM +212% NRMSE) while a statistical feature model remains comparatively stable (XGBoost +37%). Forward-fill imputation eliminates nearly all degradation from random dropout for sequence models (LSTM +57% to ~0%), but offers little help when the end of the window is corrupted. Shot-level alarm evaluation using ground-truth disruption timestamps reveals that LSTM alarm detection collapses to TPR=0.00 under proximate sensor failure, while mean-fill imputation recovers it to TPR=1.00, a reversal of the pattern observed in NRMSE. Plasma current emerges as the single most critical diagnostic across all architectures (+73% to +140% upon removal). Code, data, and trained checkpoints are available at https://github.com/Neerav-Gupta/tokamark-robustness.
发表机构
- Independent Researcher（独立研究者）
机构由 AI 辅助整理，请以论文原文为准。
