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

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.

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• Reconstructing unsteady flows from sparse, noisy measurements with a physics-constrained convolutional neural network
• Thermodynamics-informed super-resolution of scarce temporal dynamics data
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• Machine learning-based vorticity evolution and superresolution of homogeneous isotropic turbulence using wavelet projection
• Compact representation of transonic airfoil buffet flows with observable-augmented machine learning
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• Reducing Spatial Discretization Error on Coarse CFD Simulations Using an OpenFOAM-Embedded Deep Learning Framework
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• Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations
• Optimal navigation in two-dimensional flows: Control theory and reinforcement learning
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