用等离子体信号在线重建偏滤器热流分布，突破传统红外成像滞后瓶颈。
SafeDivertor: Faithful Divertor Heat Flux Reconstruction from Macroscopic Plasma State Signals via Time-Frequency Prior Exploitation
- 基于多源等离子体信号直接重构热流，无需事后建模
- 在五个指标上超越所有时序基线，达新基准性能
- 适合融合工程与算法研究者，尤其关注实时诊断的团队
偏滤器热流分析对理解磁约束聚变装置中等离子体-壁相互作用及保护面对材料至关重要。传统红外反演通常在放电后进行，需依赖设备特定的材料属性、偏滤器几何结构和边界条件的热传导建模。本文不沿用该滞后反演范式，提出一种面向在线应用的信号驱动重构新方法，直接从放电过程中可获取的多源宏观等离子体状态信号中重建时间分辨的径向热流剖面。为系统研究此任务，我们构建了 extbf{DivMPS2HF} 多源放电数据集，为信号驱动的偏滤器热流重建提供数据基础与基准。进一步提出 extbf{SafeDivertor} 任务驱动框架，应对信号重构的关键挑战：采用物理先验感知初始化为目标通道提供径向分布引导，输入扰动减少对特定异质信号的过度依赖，谱感知重建优化利用时频先验并保留瞬态动态，渐进式训练稳定多个互补目标的优化过程。在 DivMPS2HF 上的实验表明，SafeDivertor 在全部五项指标上均优于所评估的时序基线模型，确立了信号驱动偏滤器热流重建的新性能基准。源代码将发布于 https://github.com/Event-AHU/OpenFusion。
原文摘要 · Abstract (English)
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
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