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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:

Physics-informed neural networks for studying heat transfer in porous media
Jiaxuan Xu, Han Wei, Hua Bao
International Journal of Heat and Mass Transfer (2023) Vol. 217, pp. 124671-124671
Closed Access | Times Cited: 33

Showing 1-25 of 33 citing articles:

Predicting Neutron Flux Density Distribution in HTR-10 using U-Net Based on DEM-MC coupled Simulations
Qianye Yang, Nan Gui, Xingtuan Yang, et al.
Nuclear Engineering and Technology (2025), pp. 103425-103425
Open Access | Times Cited: 1
Physics-informed graph neural network based on the finite volume method for steady incompressible laminar convective heat transfer
Haiming Zhang, Xin‐Lin Xia, Ze Wu, et al.
Physics of Fluids (2025) Vol. 37, Iss. 1
Closed Access | Times Cited: 1
The Application of Physics-Informed Machine Learning in Multiphysics Modeling in Chemical Engineering
Zhi‐Yong Wu, Huan Wang, Chang He, et al.
Industrial & Engineering Chemistry Research (2023) Vol. 62, Iss. 44, pp. 18178-18204
Closed Access | Times Cited: 22
Utilizing neural networks and genetic algorithms in AI-assisted CFD for optimizing PCM-based thermal energy storage units with extended surfaces
A. Ali Rabienataj Darzi, S. Morteza Mousavi, Milad Razbin, et al.
Thermal Science and Engineering Progress (2024) Vol. 54, pp. 102795-102795
Closed Access | Times Cited: 12
A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics
Chi Zhao, Feifei Zhang, Wenqiang Lou, et al.
Physics of Fluids (2024) Vol. 36, Iss. 10
Closed Access | Times Cited: 9
Physics-informed neural network simulation of two-phase flow in heterogeneous and fractured porous media
Xia Yan, Jingqi Lin, Sheng Wang, et al.
Advances in Water Resources (2024) Vol. 189, pp. 104731-104731
Closed Access | Times Cited: 6
Hybrid Physics-Data-Driven Model for Temperature Field Prediction of Asphalt Pavement Based on Physics-Informed Neural Network
Weiwen Quan, Xianyong Ma, Z. J. Shang, et al.
(2025)
Closed Access
Physics-informed radial basis function neural network for efficiently modeling oil–water two-phase Darcy flow
Shuaijun Lv, Daolun Li, Wenshu Zha, et al.
Physics of Fluids (2025) Vol. 37, Iss. 1
Closed Access
A comparative study of dimensional and non-dimensional inputs in physics-informed and data-driven neural networks for single-droplet evaporation
Narjes Malekjani, Abdolreza Kharaghani, Evangelos Tsotsas
Chemical Engineering Science (2025), pp. 121214-121214
Open Access
Rapid Temperature Prediction of Parallel-Connected Generator Circuit Breaker Busbar Based on Multi-Input Physical Information Neural Network
Yingzhuo Li, Lixue Chen, Yinghui He
(2025)
Closed Access
Exploration of PCM melting in a heated enclosure with vertical plate fins: Experimental analysis
A. Ali Rabienataj Darzi, S. Morteza Mousavi
International Journal of Thermal Sciences (2025) Vol. 214, pp. 109884-109884
Closed Access
Tunable Thermal Conductivity of Two-Dimensional SiC Nanosheets by Grain Boundaries: Implications for the Thermo-Mechanical Sensor
Lei Huang, Kai Ren, Guoqiang Zhang, et al.
ACS Applied Nano Materials (2024) Vol. 7, Iss. 13, pp. 15078-15085
Closed Access | Times Cited: 4
Modelling of PCM based composite heat sink under varying ambient conditions: A data driven approach
Sreenath V.R., Samarjeet Chanda
Thermal Science and Engineering Progress (2025), pp. 103193-103193
Closed Access
Re-examining the input-parameters and AI strategies for Critical Heat Flux prediction
Kai Wang, Da Wang, Xiaoxing Liu, et al.
Energy (2025), pp. 134606-134606
Open Access
Adaptive physics-informed CNN based thermal performance reliability index estimation for directional heat transfer C/C composite structure in UAVs
Zelin Wang, Zhenzhou Lü
International Communications in Heat and Mass Transfer (2025) Vol. 162, pp. 108655-108655
Closed Access
Comparative Analysis of Physics-Guided Bayesian Neural Networks for Uncertainty Quantification in Dynamic Systems
Xiangzhi Xu, Julian Wang
Forecasting (2025) Vol. 7, Iss. 1, pp. 9-9
Open Access
Machine Learning Assisted Inverse Heat Transfer Problem to Find Heat Flux in Ablative Materials
MD SHARIFUL ISLAM, Prashanta Dutta
Materials Today Communications (2025), pp. 112337-112337
Open Access
A developed convolutional neural network model for accurately and stably predicting effective thermal conductivity of gradient porous ceramic materials
Pan Liu, Zelin Han, Wantong Wu, et al.
International Journal of Heat and Mass Transfer (2024) Vol. 225, pp. 125428-125428
Closed Access | Times Cited: 3
A Physics-Informed General Convolutional Network for the Computational Modeling of Materials with Damage
Jake A. Janssen, Ghadir Haikal, Erin C. DeCarlo, et al.
Journal of Computing and Information Science in Engineering (2024) Vol. 24, Iss. 11
Closed Access | Times Cited: 3
The Potential for Material Property Characterization Using Physics-Informed Neural Networks and Ultrasonic Wave Data
Sangmin Lee, John S. Popovics
Research in Nondestructive Evaluation (2024) Vol. 35, Iss. 4, pp. 211-231
Closed Access | Times Cited: 2
Data-driven methods for flow and transport in porous media: A review
Yang Guang, Ran Xu, Yusong Tian, et al.
International Journal of Heat and Mass Transfer (2024) Vol. 235, pp. 126149-126149
Open Access | Times Cited: 2
Enhancing Computational Efficiency in Porous Media Analysis: Integrating Machine Learning With Monte Carlo Ray Tracing
Farhin Tabassum, Shima Hajimirza
Journal of Thermal Science and Engineering Applications (2024) Vol. 16, Iss. 10
Closed Access | Times Cited: 1
A parameterized physics-informed machine learning approach for solving heat and mass transfer equations in the drying process
Seyedalborz Manavi, Ehsan Fattahi, Thomas Becker
International Communications in Heat and Mass Transfer (2024) Vol. 158, pp. 107897-107897
Closed Access | Times Cited: 1
Finite Element Analysis of Laminar Natural Convection in a Differentially Heated Porous Cavity Using the Darcy–Brinkman Model
Benabderrahmane Farhat, Noureddine Kaid, Sultan Alqahtani, et al.
Processes (2024) Vol. 12, Iss. 9, pp. 1974-1974
Open Access | Times Cited: 1
Prediction of critical heat flux using different methods: A review from empirical correlations to the cutting-edge machine learning
Junfeng Li, Yanping Huang, Yu Qiu, et al.
International Communications in Heat and Mass Transfer (2024) Vol. 160, pp. 108362-108362
Closed Access | Times Cited: 1
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