A non-linear complex system governed by multi-spatial and multi-temporal physics scales cannot be fully understood with a single diagnostic, as each provides only a partial view and much information is lost during data extraction. Combining multiple diagnostics also results in imperfect projections of the system's physics. By identifying hidden inter-correlations between diagnostics, we can leverage mutual support to fill in these gaps, but uncovering these inter-correlations analytically is too complex. We introduce a groundbreaking machine learning methodology to address this issue. Our multimodal approach generates super resolution data encompassing multiple physics phenomena, capturing detailed structural evolution and responses to perturbations previously unobservable. This methodology addresses a critical problem in fusion plasmas: the Edge Localized Mode (ELM), a plasma instability that can severely damage reactor walls. One method to stabilize ELM is using resonant magnetic perturbation to trigger magnetic islands. However, low spatial and temporal resolution of measurements limits the analysis of these magnetic islands due to their small size, rapid dynamics, and complex interactions within the plasma. With super-resolution diagnostics, we can experimentally verify theoretical models of magnetic islands for the first time, providing unprecedented insights into their role in ELM stabilization. This advancement aids in developing effective ELM suppression strategies for future fusion reactors like ITER and has broader applications, potentially revolutionizing diagnostics in fields such as astronomy, astrophysics, and medical imaging.

翻译：由多空间与多时间物理尺度主导的非线性复杂系统无法通过单一诊断手段被完整理解，因为每种诊断仅提供局部视角且大量信息在数据提取过程中丢失。即使结合多种诊断手段，所得结果仍是对系统物理的不完全投影。通过识别不同诊断手段间隐藏的相互关联，我们可以利用其互补性填补这些空白，但通过解析方法揭示这些关联过于复杂。本文提出一种突破性的机器学习方法以解决该问题。我们的多模态方法能够生成涵盖多种物理现象的超分辨率数据，捕获以往无法观测的精细结构演化及扰动响应。该方法针对聚变等离子体中的关键问题——边缘局域模（ELM）展开研究，这种等离子体不稳定性可能严重损坏反应堆壁。一种稳定ELM的方法是利用共振磁扰动触发磁岛。然而，测量手段的空间与时间分辨率不足，限制了针对这些磁岛的分析，因其尺度微小、动力学过程快速且在等离子体内存在复杂相互作用。借助超分辨率诊断技术，我们首次通过实验验证了磁岛的理论模型，为其在ELM稳定中的作用提供了前所未有的深入理解。这一进展有助于为ITER等未来聚变反应堆制定有效的ELM抑制策略，并具有更广泛的应用前景，可能为天文学、天体物理学及医学成像等领域的诊断技术带来革命性突破。