Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Super-resolution analysis via machine learning: a survey for fluid flows
Kai Fukami, Koji Fukagata, Kunihiko Taira
Theoretical and Computational Fluid Dynamics (2023) Vol. 37, Iss. 4, pp. 421-444
Open Access | Times Cited: 86

Showing 1-25 of 86 citing articles:

Reconstruction of the solid–liquid two-phase flow field in the pipeline based on limited pipeline wall information
Shengpeng Xiao, Chuyi Wan, Hongbo Zhu, et al.
Physics of Fluids (2025) Vol. 37, Iss. 2
Closed Access | Times Cited: 1
Can Artificial Intelligence Accelerate Fluid Mechanics Research?
Dimitris Drikakis, Filippos Sofos
Fluids (2023) Vol. 8, Iss. 7, pp. 212-212
Open Access | Times Cited: 28
A deep learning super-resolution model for turbulent image upscaling and its application to shock wave–boundary layer interaction
Filippos Sofos, Dimitris Drikakis, Ioannis W. Kokkinakis, et al.
Physics of Fluids (2024) Vol. 36, Iss. 2
Open Access | Times Cited: 12
Data-driven nonlinear turbulent flow scaling with Buckingham Pi variables
Kai Fukami, Susumu Goto, Kunihiko Taira
Journal of Fluid Mechanics (2024) Vol. 984
Open Access | Times Cited: 11
The transition to sustainable combustion: Hydrogen- and carbon-based future fuels and methods for dealing with their challenges
Heinz Pitsch
Proceedings of the Combustion Institute (2024) Vol. 40, Iss. 1-4, pp. 105638-105638
Open Access | Times Cited: 11
Influence of adversarial training on super-resolution turbulence reconstruction
Ludovico Nista, Heinz Pitsch, Christoph Schumann, et al.
Physical Review Fluids (2024) Vol. 9, Iss. 6
Closed Access | Times Cited: 8
Two-stage super-resolution simulation method of three-dimensional street-scale atmospheric flows for real-time urban micrometeorology prediction
Yuki Yasuda, Ryo Onishi
Urban Climate (2025) Vol. 59, pp. 102300-102300
Open Access | Times Cited: 1
Reduced-order modeling of fluid flows with transformers
AmirPouya Hemmasian, Amir Barati Farimani
Physics of Fluids (2023) Vol. 35, Iss. 5
Closed Access | Times Cited: 19
WindSeer: real-time volumetric wind prediction over complex terrain aboard a small uncrewed aerial vehicle
Florian Achermann, Thomas Stastny, Bogdan Danciu, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 7
Deep learning architecture for sparse and noisy turbulent flow data
Filippos Sofos, Dimitris Drikakis, Ioannis W. Kokkinakis
Physics of Fluids (2024) Vol. 36, Iss. 3
Closed Access | Times Cited: 5
Super-resolution of three-dimensional temperature and velocity for building-resolving urban micrometeorology using physics-guided convolutional neural networks with image inpainting techniques
Yuki Yasuda, Ryo Onishi, Keigo Matsuda
Building and Environment (2023) Vol. 243, pp. 110613-110613
Open Access | Times Cited: 13
Spatial superresolution based on simultaneous dual PIV measurement with different magnification
Yuta Ozawa, Harutaka HONDA, Taku Nonomura
Experiments in Fluids (2024) Vol. 65, Iss. 4
Closed Access | Times Cited: 5
Data and physics-driven modeling for fluid flow with a physics-informed graph convolutional neural network
Jiang-Zhou Peng, Yue Hua, Nadine Aubry, et al.
Ocean Engineering (2024) Vol. 301, pp. 117551-117551
Closed Access | Times Cited: 5
A two-stage CFD-GNN approach for efficient steady-state prediction of urban airflow and airborne contaminant dispersion
Runmin Zhao, Sumei Liu, Junjie Liu, et al.
Sustainable Cities and Society (2024) Vol. 112, pp. 105607-105607
Closed Access | Times Cited: 5
High-fidelity reconstruction of large-area damaged turbulent fields with a physically constrained generative adversarial network
Qinmin Zheng, Tianyi Li, Benteng Ma, et al.
Physical Review Fluids (2024) Vol. 9, Iss. 2
Closed Access | Times Cited: 4
Interpreting and generalizing deep learning in physics-based problems with functional linear models
Amirhossein Arzani, Lingxiao Yuan, Pania Newell, et al.
Engineering With Computers (2024)
Closed Access | Times Cited: 4
Data-driven transient lift attenuation for extreme vortex gust–airfoil interactions
Kai Fukami, Hiroya Nakao, Kunihiko Taira
Journal of Fluid Mechanics (2024) Vol. 992
Closed Access | Times Cited: 4
Super-resolution of turbulence with dynamics in the loss
Jacob Page
Journal of Fluid Mechanics (2025) Vol. 1002
Closed Access
Enhancing indoor temperature mapping: High-resolution insights through deep learning and computational fluid dynamics
Filippos Sofos, Dimitris Drikakis, Ioannis W. Kokkinakis
Physics of Fluids (2025) Vol. 37, Iss. 1
Closed Access
On the spatial prediction of the turbulent flow behind an array of cylinders via echo state networks
Mohammad Sharifi Ghazijahani, Christian Cierpka
Engineering Applications of Artificial Intelligence (2025) Vol. 144, pp. 110079-110079
Open Access
Super-resolution reconstruction of sequential images based on an active shift via a hybrid attention calibration mechanism
Qiang Wu, Ziyi Yang, Hongfei Zeng, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 144, pp. 110178-110178
Closed Access
A multi-scale hybrid attention Swin-transformer-based model for the super-resolution reconstruction of turbulence
Xiuyan Liu, Yufei Zhang, Tingting Guo, et al.
Nonlinear Dynamics (2025)
Closed Access
Reconstructing unsteady flows from sparse, noisy measurements with a physics-constrained convolutional neural network
Yaxin Mo, Luca Magri
Physical Review Fluids (2025) Vol. 10, Iss. 3
Open Access
Introduction to turbulence & learning from data
Karthik Duraisamy, Steven L. Brunton, Kunihiko Taira
Elsevier eBooks (2025), pp. 1-25
Closed Access
A prediction of urban boundary layer using Recurrent Neural Network and reduced order modeling
Yedam Lee, Sang Lee
Building and Environment (2025), pp. 112804-112804
Closed Access
Page 1 - Next Page

Scroll to top