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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:

From Computational Fluid Dynamics to Structure Interpretation via Neural Networks: An Application to Flow and Transport in Porous Media
Agnese Marcato, Gianluca Boccardo, Daniele Marchisio
Industrial & Engineering Chemistry Research (2022) Vol. 61, Iss. 24, pp. 8530-8541
Open Access | Times Cited: 38

Showing 1-25 of 38 citing articles:

Hybrid-nanofluid magneto-convective flow and porous media contribution to entropy generation
Fateh Mebarek‐Oudina, Ines Chabani, Hanumesh Vaidya, et al.
International Journal of Numerical Methods for Heat & Fluid Flow (2024) Vol. 34, Iss. 2, pp. 809-836
Closed Access | Times Cited: 71
Can Artificial Intelligence Accelerate Fluid Mechanics Research?
Dimitris Drikakis, Filippos Sofos
Fluids (2023) Vol. 8, Iss. 7, pp. 212-212
Open Access | Times Cited: 24
Enhancing Mass Transfer Coefficient Prediction from Field Emission Scanning Electron Microscope Images Through Convolutional Neural Networks and Data Augmentation Techniques
Agnese Marcato, Gianluca Boccardo, Roberto Pisano
Processes (2025) Vol. 13, Iss. 2, pp. 365-365
Open Access
Advanced ANN computational procedure for thermal transport prediction in polymer-based ternary radiative Carreau nanofluid with extreme shear rates over bullet surface
Adil Darvesh, Fethi Mohamed Maiz, Basma Souayeh, et al.
Applied Rheology (2025) Vol. 35, Iss. 1
Open Access
Materials processing model-driven discovery framework for porous materials using machine learning and genetic algorithm: A focus on optimization of permeability and filtration efficiency
Tomoki Yasuda, Shinichi Ookawara, Shiro Yoshikawa, et al.
Chemical Engineering Journal (2022) Vol. 453, pp. 139540-139540
Open Access | Times Cited: 31
Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework
Agnese Marcato, Javier E. Santos, Chaoyue Liu, et al.
Energy storage materials (2023) Vol. 63, pp. 102927-102927
Open Access | Times Cited: 14
Neural Network Model Using Levenberg Marquardt Backpropagation Algorithm for the Prandtl Fluid Flow Over Stratified Curved Sheet
Pradeep Kumar, F. Almeida, B. Nagaraja, et al.
IEEE Access (2024) Vol. 12, pp. 102242-102260
Open Access | Times Cited: 4
Intensification of catalytic reactors: A synergic effort of Multiscale Modeling, Machine Learning and Additive Manufacturing
Mauro Bracconi
Chemical Engineering and Processing - Process Intensification (2022) Vol. 181, pp. 109148-109148
Closed Access | Times Cited: 24
Application of deep learning neural networks for the analysis of fluid-particle dynamics in fibrous filters
Mohammadreza Shirzadi, Tomonori Fukasawa, Kunihiro Fukui, et al.
Chemical Engineering Journal (2022) Vol. 455, pp. 140775-140775
Closed Access | Times Cited: 21
Reconciling deep learning and first‐principle modelling for the investigation of transport phenomena in chemical engineering
Agnese Marcato, Daniele Marchisio, Gianluca Boccardo
The Canadian Journal of Chemical Engineering (2023) Vol. 101, Iss. 6, pp. 3013-3018
Closed Access | Times Cited: 11
Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network
Agnese Marcato, Javier E. Santos, Gianluca Boccardo, et al.
Chemical Engineering Journal (2022) Vol. 455, pp. 140367-140367
Open Access | Times Cited: 19
A computationally efficient modeling of flow in complex porous media by coupling multiscale digital rock physics and deep learning: Improving the tradeoff between resolution and field-of-view
Iman Nabipour, Amir Raoof, Veerle Cnudde, et al.
Advances in Water Resources (2024) Vol. 188, pp. 104695-104695
Open Access | Times Cited: 3
Machine Learning and Data Science in Chemical Engineering
Hanyu Gao, Li‐Tao Zhu, Zheng‐Hong Luo, et al.
Industrial & Engineering Chemistry Research (2022) Vol. 61, Iss. 24, pp. 8357-8358
Closed Access | Times Cited: 17
Progress and future directions bridging microplastics transport from pore to continuum scale: A comprehensive review for experimental and modeling approaches
Seung Ji Lim, Kyung‐Jin Lee, Hansung Nam, et al.
TrAC Trends in Analytical Chemistry (2024) Vol. 179, pp. 117851-117851
Open Access | Times Cited: 2
Numerical Modelling on Dispersion Behavior of Particulate Contamination Induced by a Moving Operator in a Semiconductor Cleanroom: A Eulerian-Eulerian Method
Yao Chengxi, Seungjae Lee, Huh Dongbin, et al.
Journal of Building Engineering (2024) Vol. 96, pp. 110409-110409
Closed Access | Times Cited: 2
Enhanced intelligent approach for determination of crude oil viscosity at reservoir conditions
Kiana Peiro Ahmady Langeroudy, Parsa Kharazi Esfahani, Mohammad Reza Khorsand Movaghar
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 7
A Dataset of 3D Structural and Simulated Transport Properties of Complex Porous Media
Javier E. Santos, Bernard Chang, Alex Gigliotti, et al.
Scientific Data (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 12
Prediction of 3D Velocity Field of Reticulated Foams Using Deep Learning for Transport Analysis
Danny D. Ko, Hangjie Ji, Y. Sungtaek Ju
Transport in Porous Media (2023) Vol. 148, Iss. 3, pp. 577-604
Open Access | Times Cited: 6
Flow and mass transfer prediction in anisotropic TPMS-structures as extracorporeal oxygenator membranes using reduced order modeling
Kai P. Barbian, Lukas T. Hirschwald, John Linkhorst, et al.
Journal of Membrane Science (2023) Vol. 690, pp. 122160-122160
Closed Access | Times Cited: 5
Graph convolution network-based surrogate model for natural convection in annuli
Feng Feng, Yubai Li, Zhihua Chen, et al.
Case Studies in Thermal Engineering (2024) Vol. 57, pp. 104330-104330
Open Access | Times Cited: 1
Learning a general model of single phase flow in complex 3D porous media
Javier E. Santos, Agnese Marcato, Qinjun Kang, et al.
Machine Learning Science and Technology (2024) Vol. 5, Iss. 2, pp. 025039-025039
Open Access | Times Cited: 1
Prediction of three-dimensional flow field inside realistic fibrous filter obtained from x-ray computed tomography images using deep convolutional neural networks
Kodai Hada, Mohammadreza Shirzadi, Tomonori Fukasawa, et al.
Physics of Fluids (2024) Vol. 36, Iss. 6
Closed Access | Times Cited: 1
Enforcing global constraints for the dispersion closure problem: τ2-SIMPLE algorithm
Ross M. Weber, Bowen Ling, Ilenia Battiato
Advances in Water Resources (2024) Vol. 191, pp. 104759-104759
Closed Access | Times Cited: 1
Scaling the predictions of multiphase flow through porous media using operator learning
Nidhish Jain, Shantanu Roy, Hariprasad Kodamana, et al.
Chemical Engineering Journal (2024) Vol. 503, pp. 157671-157671
Closed Access | Times Cited: 1
A diffused-interface model for the lyophilization of a packed bed of spray-frozen particles
Lorenzo Stratta, Merve B. Adali, Antonello Barresi, et al.
Chemical Engineering Science (2023) Vol. 275, pp. 118726-118726
Open Access | Times Cited: 4
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