SafeDivertor：利用时频先验从宏观等离子体状态信号忠实重构偏滤器热通量
SafeDivertor: Faithful Divertor Heat Flux Reconstruction from Macroscopic Plasma State Signals via Time-Frequency Prior Exploitation
- School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）
- Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
- School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）
- Institute of Plasma Physics, Chinese Academy of Sciences（中国科学院等离子体物理研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对磁约束聚变装置偏滤器热通量重构需求，提出SafeDivertor框架，构建DivMPS2HF数据集，实现从宏观等离子体信号在线重构热通量，性能优于基线，建立新基准。
AI中文摘要：
偏滤器热通量分析对理解磁约束聚变装置中等离子体与壁的相互作用、保护面向等离子体部件至关重要，而传统基于红外的反演通常在放电结束后进行，需要结合装置特定的材料属性、偏滤器几何结构及边界条件的热传导建模。本文未加速该传统红外反演范式，而是引入一种新的面向在线的基于信号的重构范式，可直接从放电期间可用的多源宏观等离子体状态信号中重构随时间变化的径向热通量剖面。为支持该任务的系统研究，本文构建了DivMPS2HF多源放电数据集，为基于信号的偏滤器热通量重构提供数据基础与基准。本文进一步提出SafeDivertor，这是一种任务驱动的框架，旨在解决基于信号的热通量重构的关键挑战：它采用物理先验感知初始化，为目标通道提供径向分布指导；通过输入扰动减少对特定异质信号的过度依赖；采用频谱感知的重构优化，以利用时频先验并保留瞬态动力学；还采用渐进式训练以稳定这些互补目标的优化。在DivMPS2HF上的实验表明，SafeDivertor在所有五个指标上均优于评估的时间序列基线，取得了最佳整体性能，为基于信号的偏滤器热通量重构建立了新的性能基准，源代码将在该httpsURL发布。
英文摘要：
Divertor heat-flux analysis is essential for understanding plasma-wall interactions and protecting plasma-facing components in magnetic-confinement fusion devices, while conventional infrared-based inversion is usually performed after discharge and requires heat-conduction modeling with device-specific material properties, divertor geometry, and boundary conditions. Rather than accelerating this conventional infrared-based inversion paradigm, we introduce a new online-oriented signal-based reconstruction paradigm that directly reconstructs time-resolved radial heat-flux profiles from multi-source macroscopic plasma-state signals available during discharge. To enable systematic study of this task, we construct \textbf{DivMPS2HF}, a multi-source discharge dataset that provides the data foundation and benchmark for signal-based divertor heat-flux reconstruction. We further propose \textbf{SafeDivertor}, a task-driven framework designed to address the key challenges of signal-based heat-flux reconstruction. It employs physical prior-aware initialization to provide radial-distribution guidance for target channels, input perturbation to reduce over-reliance on specific heterogeneous signals, spectral-aware reconstruction optimization to exploit time-frequency priors and preserve transient dynamics, and progressive training to stabilize the optimization of these complementary objectives. Experiments on DivMPS2HF demonstrate that SafeDivertor achieves the best overall performance among the evaluated time-series baselines across all five metrics, establishing a new performance benchmark for signal-based divertor heat-flux reconstruction. The source code will be released on https://github.com/Event-AHU/OpenFusion
