发表机构
Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University; Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences; School of Information Science and Technology, University of Science and Technology of China（安徽大学计算机科学与技术学院; 中国科学院合肥物质科学研究院等离子体物理研究所; 中国科学技术大学信息科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对EAST托卡马克中离子和电子温度剖面预测，提出融合全局诊断信号与光谱数据的注意力神经网络，实现联合预测，克服光谱缺失问题，提升预测性能。
AI 中文摘要
离子温度Ti和电子温度Te是评估托卡马克等离子体约束、湍流输运和磁流体动力学稳定性的基本动力学参数。可靠预测它们的时空剖面对于实验先进超导托卡马克（EAST）上的长脉冲稳态运行至关重要。然而，现有的EAST诊断系统存在明显的测量局限性，包括电荷交换复合光谱的适用条件狭窄以及长放电期间边缘温度诊断信号的严重退化。经典的基于物理的输运代码存在高计算成本和误差累积的问题，而现有的数据驱动模型仅依赖光谱数据分别预测Ti或Te，缺乏Ti/Te联合建模，并且在光谱测量缺失时失效。在这项工作中，我们提出了一种多模态（即全局宏观诊断信号和光谱数据）融合注意力神经网络，用于在EAST上同时重建Ti/Te剖面。它利用自适应注意力模块对全局诊断特征进行加权并抑制噪声干扰，采用卷积层提取深层光谱表示，并利用多头自注意力促进跨模态特征交互。构建了两个独立的预测头以输出Ti和Te的完整径向分布。这项工作建立了第一个面向EAST的联合Ti/Te预测模型，兼容没有光谱诊断的运行场景。层次注意力机制挖掘非线性多模态相关性，以实现高性能的双温度剖面预测。本文的源代码可在该https URL上获取。
英文摘要
Ion temperature Ti and electron temperature Te are fundamental kinetic parameters for evaluating tokamak plasma confinement, turbulent transport and magnetohydrodynamic stability. Reliable prediction of their spatiotemporal profiles is essential for long-pulse steady-state operation on the Experimental Advanced Superconducting Tokamak (EAST). However, existing EAST diagnostic systems have obvious measurement limitations, including narrow applicable conditions of charge exchange recombination spectroscopy and severe signal degradation of edge temperature diagnostics during long discharges. Classical physics-based transport codes suffer from high computational cost and error accumulation, while existing data-driven models only separately predict Ti or Te relying on spectral data, lacking joint Ti/Te modeling and failing under missing spectral measurements. In this work, we propose a multi-modal (i.e., global macroscopic diagnostic signals and spectral data) fusion attention neural network for simultaneous reconstruction of Ti/Te profiles on EAST. It leverages an adaptive attention module to weight global diagnostic features and suppress noise interference, employs convolutional layers to extract deep spectral representations, and utilizes multi-head self-attention to facilitate cross-modal feature interaction. Two separate prediction heads are constructed to output full radial distributions of Ti and Te. This work builds the first EAST-oriented joint Ti/Te prediction model compatible with operating scenarios without spectral diagnostics. Hierarchical attention mechanisms excavate nonlinear multi-modal correlations to realize high-performance dual-temperature profile prediction. The source code of this paper is available on https://github.com/Event-AHU/OpenFusion/tree/main/TiTe_Prediction
