Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows
This is an incremental survey paper that reviews existing methods for super-resolution analysis in fluid dynamics applications.
This paper surveys machine-learning-based super-resolution reconstruction techniques for vortical flows, demonstrating that physics-inspired model designs can successfully reconstruct high-resolution flow fields from low-resolution data in case studies of two-dimensional decaying isotropic turbulence.
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.