Super-resolution analysis via machine learning: a survey for fluid flows

Fukami Kai; Fukagata Koji; Taira Kunihiko

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Super-resolution analysis via machine learning: a survey for fluid flows

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This paper surveys machine-learning-based super-resolution reconstruction for vortical flows. Super resolution aims to find the high-resolution flowfields 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 limitedmeasurements. We also discuss the challenges and outlooks of machine-learning-based superresolution analysis for fluid flow applications. The insights gained from this study can be leveraged for superresolution analysis of numerical and experimental flow data.

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《Theoretical and Computational Fluid Dynamics》 |2023年第4期|421-444|共24页
作者
Fukami Kai; Fukagata Koji; Taira Kunihiko;
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作者单位

Univ Calif Los Angeles;

Keio Univ;

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原文格式 PDF
正文语种英语
中图分类流体力学;
关键词
Machine learning center dot Super resolution center dot Vortex-dominated flows center dot Turbulence; ARTIFICIAL NEURAL-NETWORKS; IMAGE SUPERRESOLUTION; STOCHASTIC ESTIMATION; STATE ESTIMATION; RECONSTRUCTION; SPARSE; PREDICTION; SIMULATION; TURBULENCE; DIMENSIONALITY;

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Super-resolution analysis via machine learning: a survey for fluid flows

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