2021
DOI: 10.1017/jfm.2020.1028
|View full text |Cite
|
Sign up to set email alerts
|

Unsupervised deep learning for super-resolution reconstruction of turbulence

Abstract: Abstract

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

1
87
0
1

Year Published

2021
2021
2023
2023

Publication Types

Select...
5
3
1
1

Relationship

0
10

Authors

Journals

citations
Cited by 151 publications
(89 citation statements)
references
References 55 publications
1
87
0
1
Order By: Relevance
“…In addition, machine learning methods have been employed for super-resolution reconstruction of turbulent flows (Maulik & San 2017;Fukami, Fukagata & Taira 2019;Kim & Lee 2020;Liu et al 2020;Yuan et al 2020;Kim et al 2021). Such reconstruction can be used to improve predictions based on coarse wall measurements in wall-bounded turbulence (Güemes et al 2021;Vinuesa & Brunton 2021).…”
Section: Introductionmentioning
confidence: 99%
“…In addition, machine learning methods have been employed for super-resolution reconstruction of turbulent flows (Maulik & San 2017;Fukami, Fukagata & Taira 2019;Kim & Lee 2020;Liu et al 2020;Yuan et al 2020;Kim et al 2021). Such reconstruction can be used to improve predictions based on coarse wall measurements in wall-bounded turbulence (Güemes et al 2021;Vinuesa & Brunton 2021).…”
Section: Introductionmentioning
confidence: 99%
“…Figure 1: Schematic illustration of GAN architecture (adapted from Kim et al (2021)). The novel element is the use for training and testing of gappy high-resolution fields provided directly by binned PTV data.…”
Section: Methodology 21 Piv-tailored Ganmentioning
confidence: 99%
“…While LES may be practiced in isolation from specific concerns of a consistent framework, a specific definition of that which an LES aspires to accurately reproduce is required for advanced techniques such as data-driven closure (Sarghini, de Felice & Santini 2003;Moreau, Teytaud & Bertoglio 2006;Gamahara & Hattori 2017;Vollant, Balarac & Corre 2017;Wang et al 2018;Beck, Flad & Munz 2019;Cheng et al 2019;Yang et al 2019;Zhou et al 2019;Sirignano, MacArt & Freund 2020;Xie, Wang & Weinan 2020a;Xie, Yuan & Wang 2020b;Yuan, Xie & Wang 2020;Bode et al 2021;Duraisamy 2021;Freund & Ferrante 2021;Park & Choi 2021;Portwood et al 2021;Prakash, Jansen & Evans 2021;Stoffer et al 2021;Wang et al 2021) and super-resolution enrichment (Domaradzki & Loh 1999;Scotti & Meneveau 1999;Stolz & Adams 1999;Milano & Koumoutsakos 2002;Leonard 2016;Ghate & Lele 2017;Maulik & San 2017;Bassenne et al 2019;Wang, Zhao & Ihme 2019;Ghate & Lele 2020;Liu et al 2020;Kim et al 2021). For example, without a clear definition of what an LES solution should represent, one cannot train a neural network to serve as a sub-grid closure in a robust way.…”
Section: Introductionmentioning
confidence: 99%