This paper surveys machine-learning-based super-resolution reconstruction for vortical flows. Super resolution aims to find the high-resolution flow fields from low-resolution data and is generally an approach used in image reconstruction. In addition to surveying a variety of recent super-resolution applications, we provide case studies of super-resolution analysis for an example of two-dimensional decaying isotropic turbulence. We demonstrate that physics-inspired model designs enable successful reconstruction of vortical flows from spatially limited measurements. We also discuss the challenges and outlooks of machine-learning-based super-resolution analysis for fluid flow applications. The insights gained from this study can be leveraged for super-resolution analysis of numerical and experimental flow data.

翻译：本文综述了基于机器学习的涡旋流动超分辨率重建方法。超分辨率旨在从低分辨率数据中重建高分辨率流场，通常应用于图像重建领域。除回顾近期各类超分辨率应用外，我们以二维衰减各向同性湍流为例进行了超分辨率分析案例研究。研究表明，基于物理启发的模型设计能够成功地从空间受限的测量数据中重建涡旋流动。我们还讨论了基于机器学习的超分辨率分析在流体流动应用中的挑战与前景。本研究获得的见解可推广至数值及实验流动数据的超分辨率分析。