托卡马克偏滤器中中性粒子发射断层扫描的可见光成像诊断：基于高效Transformer的替代模型
Visible-Light Imaging Diagnosis of Neutral Particle Emission Tomography in the Tokamak Divertor: An Efficient Transformer-based Surrogate Model
- School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）
- School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）
- Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥物质科学研究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：
本文针对托卡马克偏滤器中中性粒子发射断层扫描，提出基于差分Transformer的Delta-InvFormer模型，利用连续视频帧捕捉等离子体动态，经时空差分自注意力提取特征，融合后预测光强分布，相比传统方法显著加速且精度有竞争力。
AI中文摘要：
近年来核聚变取得显著进展，有望成为应对全球能源挑战的重要途径之一。本文聚焦于利用可见光相机观测等离子体，分析其时空运动线索并预测光强的二维空间分布，为未来基于深度神经网络的科学实验提供基础。具体提出了以差分Transformer为核心的新型骨干网络Delta-InvFormer。通过将连续视频帧作为输入，能更好捕捉等离子体动态，时空差分自注意力有效减轻噪声信号干扰以确保高质量特征提取，将这些特征融合成紧凑且信息丰富的表示，输入解码器网络预测分布。基于从EAST大型科学装置收集的实际实验数据，结果表明该模型不仅显著加速传统分布预测方法，还实现了有竞争力的重建精度，相关代码将在指定网址发布。
英文摘要：
Nuclear fusion has made significant progress in recent years and is expected to become one of the most important pathways to addressing global energy challenges. This paper focuses on observing plasma using visible-light cameras, analyzing its spatio-temporal motion cues, and predicting the two-dimensional spatial distribution of light intensity, aiming to provide a foundational basis for future scientific experiments using deep neural networks. Specifically, we propose Delta-InvFormer, a novel backbone network centered on a differential Transformer. The key insight is that by taking consecutive video frames as input, we can better capture the dynamics of the plasma. Moreover, spatial and temporal differential self-attention effectively mitigates interference from noisy signals, ensuring high-quality feature extraction. These features are then fused into a compact and informative representation, which is fed into a decoder network to predict the distribution. Based on real experimental data collected from the Experimental Advanced Superconducting Tokamak (EAST) large-scale scientific facility, our results demonstrate that the proposed model not only significantly accelerates traditional methods for distribution prediction but also achieves competitive reconstruction accuracy. The source code of this paper will be released on https://github.com/Event-AHU/OpenFusion
