Abstract

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.

Graphical abstract

中文翻译：

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通过机器学习进行超分辨率分析：流体流动调查

摘要

本文调查了基于机器学习的涡流超分辨率重建。超分辨率旨在从低分辨率数据中找到高分辨率的流场，通常是图像重建中使用的一种方法。除了调查最近的各种超分辨率应用之外，我们还提供了二维衰减各向同性湍流示例的超分辨率分析案例研究。我们证明了受物理启发的模型设计能够从空间有限的测量中成功重建涡流。我们还讨论了基于机器学习的流体流动应用超分辨率分析的挑战和前景。从这项研究中获得的见解可用于数值和实验流数据的超分辨率分析。

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