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OpenAlex Citation Counts

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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:

Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows
Hamidreza Eivazi, Soledad Le Clainche, Sergio Hoyas, et al.
Expert Systems with Applications (2022) Vol. 202, pp. 117038-117038
Open Access | Times Cited: 92

Showing 1-25 of 92 citing articles:

Enhancing computational fluid dynamics with machine learning
Ricardo Vinuesa, Steven L. Brunton
Nature Computational Science (2022) Vol. 2, Iss. 6, pp. 358-366
Open Access | Times Cited: 296
A review of physics-based machine learning in civil engineering
Shashank Reddy Vadyala, Sai Nethra Betgeri, John C. Matthews, et al.
Results in Engineering (2021) Vol. 13, pp. 100316-100316
Open Access | Times Cited: 149
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: 88
Improving aircraft performance using machine learning: A review
Soledad Le Clainche, Esteban Ferrer, S. Gibson, et al.
Aerospace Science and Technology (2023) Vol. 138, pp. 108354-108354
Open Access | Times Cited: 79
Deep reinforcement learning for turbulent drag reduction in channel flows
Luca Guastoni, Jean Rabault, Philipp Schlatter, et al.
The European Physical Journal E (2023) Vol. 46, Iss. 4
Open Access | Times Cited: 60
A transformer-based synthetic-inflow generator for spatially developing turbulent boundary layers
Mustafa Z. Yousif, Meng Zhang, Linqi Yu, et al.
Journal of Fluid Mechanics (2023) Vol. 957
Open Access | Times Cited: 50
Discovering causal relations and equations from data
Gustau Camps‐Valls, Andreas Gerhardus, Urmi Ninad, et al.
Physics Reports (2023) Vol. 1044, pp. 1-68
Open Access | Times Cited: 45
β-Variational autoencoders and transformers for reduced-order modelling of fluid flows
Alberto Solera-Rico, Carlos Sanmiguel Vila, M.A. Gómez, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 40
Three-dimensional ESRGAN for super-resolution reconstruction of turbulent flows with tricubic interpolation-based transfer learning
Linqi Yu, Mustafa Z. Yousif, Meng Zhang, et al.
Physics of Fluids (2022) Vol. 34, Iss. 12
Open Access | Times Cited: 56
Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning
Ricardo Vinuesa, O. Lehmkuhl, Adrian Lozano-Durán, et al.
Fluids (2022) Vol. 7, Iss. 2, pp. 62-62
Open Access | Times Cited: 51
Comparing different nonlinear dimensionality reduction techniques for data-driven unsteady fluid flow modeling
Hunor Csala, Scott T. M. Dawson, Amirhossein Arzani
Physics of Fluids (2022) Vol. 34, Iss. 11
Closed Access | Times Cited: 40
Data-driven modal decomposition methods as feature detection techniques for flow problems: A critical assessment
Beka Begiashvili, Nourelhouda Groun, Jesús Garicano‐Mena, et al.
Physics of Fluids (2023) Vol. 35, Iss. 4
Open Access | Times Cited: 34
Aeroacoustic airfoil shape optimization enhanced by autoencoders
Jiaqing Kou, Laura Botero-Bolívar, Román Ballano, et al.
Expert Systems with Applications (2023) Vol. 217, pp. 119513-119513
Open Access | Times Cited: 26
Perspectives on predicting and controlling turbulent flows through deep learning
Ricardo Vinuesa
Physics of Fluids (2024) Vol. 36, Iss. 3
Open Access | Times Cited: 11
Using diffusion models for reducing spatiotemporal errors of deep learning based urban microclimate predictions at post-processing stage
Sepehrdad Tahmasebi, Geng Tian, Shaoxiang Qin, et al.
Physics of Fluids (2025) Vol. 37, Iss. 3
Closed Access | Times Cited: 1
A review of advances towards efficient reduced-order models (ROM) for predicting urban airflow and pollutant dispersion
Shahin Masoumi-Verki, Fariborz Haghighat, Ursula Eicker
Building and Environment (2022) Vol. 216, pp. 108966-108966
Closed Access | Times Cited: 34
Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression
Masaki Morimoto, Kai Fukami, Romit Maulik, et al.
Physica D Nonlinear Phenomena (2022) Vol. 440, pp. 133454-133454
Open Access | Times Cited: 28
Enhancing Computational Fluid Dynamics with Machine Learning
Ricardo Vinuesa
(2022)
Open Access | Times Cited: 28
Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning
Joongoo Jeon, Juhyeong Lee, Ricardo Vinuesa, et al.
International Journal of Heat and Mass Transfer (2023) Vol. 220, pp. 124900-124900
Closed Access | Times Cited: 20
Towards optimal β-variational autoencoders combined with transformers for reduced-order modelling of turbulent flows
Yuning Wang, Alberto Solera-Rico, Carlos Sanmiguel Vila, et al.
International Journal of Heat and Fluid Flow (2023) Vol. 105, pp. 109254-109254
Open Access | Times Cited: 17
Finite volume method network for the acceleration of unsteady computational fluid dynamics: Non‐reacting and reacting flows
Joongoo Jeon, Juhyeong Lee, Sung Joong Kim
International Journal of Energy Research (2022) Vol. 46, Iss. 8, pp. 10770-10795
Open Access | Times Cited: 24
Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives
Michele Buzzicotti
EPL (Europhysics Letters) (2023) Vol. 142, Iss. 2, pp. 23001-23001
Open Access | Times Cited: 14
Unsteady reduced order model with neural networks and flight-physics-based regularization for aerodynamic applications
Mateus Dias Ribeiro, Mario Stradtner, Philipp Bekemeyer
Computers & Fluids (2023) Vol. 264, pp. 105949-105949
Closed Access | Times Cited: 13
Data-driven nonlinear parametric model order reduction framework using deep hierarchical variational autoencoder
Si-Hun Lee, Sangmin Lee, Kijoo Jang, et al.
Engineering With Computers (2024) Vol. 40, Iss. 4, pp. 2385-2400
Open Access | Times Cited: 5
Development of Aerodynamic Database to Characterize Novel Wing Shapes for Electrified, Hybrid, or Hydrogen Aircraft
Katherine J. Asztalos, Francesco Salucci, Nirmit Prabhakar
AIAA SCITECH 2022 Forum (2025)
Closed Access
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