Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows

TL;DR

This survey reviews machine-learning-based super-resolution techniques for fluid flows, highlighting physics-inspired models that enable high-resolution flow reconstruction from limited data, with case studies on turbulence.

Contribution

It provides a comprehensive overview of recent super-resolution applications in fluid dynamics and demonstrates the effectiveness of physics-inspired models through case studies.

Findings

Physics-inspired models enable successful vortical flow reconstruction.

Super-resolution can recover high-resolution flow fields from limited measurements.

The survey discusses challenges and future outlooks in the field.

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.