This paper introduces a groundbreaking multi-modal neural network model designed for resolution enhancement, which innovatively leverages inter-diagnostic correlations within a system. Traditional approaches have primarily focused on uni-modal enhancement strategies, such as pixel-based image enhancement or heuristic signal interpolation. In contrast, our model employs a novel methodology by harnessing the diagnostic relationships within the physics of fusion plasma. Initially, we establish the correlation among diagnostics within the tokamak. Subsequently, we utilize these correlations to substantially enhance the temporal resolution of the Thomson Scattering diagnostic, which assesses plasma density and temperature. By increasing its resolution from conventional 200Hz to 500kHz, we facilitate a new level of insight into plasma behavior, previously attainable only through computationally intensive simulations. This enhancement goes beyond simple interpolation, offering novel perspectives on the underlying physical phenomena governing plasma dynamics.

翻译：本文提出了一种开创性的多模态神经网络模型，旨在提升分辨率，创新性地利用了系统内部诊断信号间的相关性。传统方法主要聚焦于单模态增强策略，如基于像素的图像增强或启发式信号插值。相比之下，我们的模型采用了一种新颖的方法，通过挖掘聚变等离子体物理中的诊断关系来发挥作用。首先，我们建立了托卡马克装置内各诊断信号之间的相关性。随后，我们利用这些相关性显著提升了测量等离子体密度与温度的汤姆逊散射诊断的时间分辨率。通过将其分辨率从传统的200Hz提升至500kHz，我们为洞察等离子体行为开辟了全新层次，而此前唯有通过计算密集型模拟才能达到这一深度。这种增强超越了简单的插值，为支配等离子体动力学的基础物理现象提供了全新视角。