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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 network for inverse modeling of natural-state geothermal systems
Kazuya Ishitsuka, Weiren Lin
Applied Energy (2023) Vol. 337, pp. 120855-120855
Open Access | Times Cited: 20

Showing 20 citing articles:

Physics-informed machine learning for reservoir management of enhanced geothermal systems
Bicheng Yan, Zhen Xu, Manojkumar Gudala, et al.
Geoenergy Science and Engineering (2024) Vol. 234, pp. 212663-212663
Closed Access | Times Cited: 8
Hydrogen jet and diffusion modeling by physics-informed graph neural network
Xinqi Zhang, Jihao Shi, Junjie Li, et al.
Renewable and Sustainable Energy Reviews (2024) Vol. 207, pp. 114898-114898
Closed Access | Times Cited: 8
A self-supervised learning framework based on physics-informed and convolutional neural networks to identify local anisotropic permeability tensor from textiles 2D images for filling pattern prediction
John M. Hanna, José V. Aguado, Sébastien Comas-Cardona, et al.
Composites Part A Applied Science and Manufacturing (2024) Vol. 179, pp. 108019-108019
Open Access | Times Cited: 7
Fractional rheology-informed neural networks for data-driven identification of viscoelastic constitutive models
Donya Dabiri, Milad Saadat, Deepak Mangal, et al.
Rheologica Acta (2023) Vol. 62, Iss. 10, pp. 557-568
Open Access | Times Cited: 14
The Application of Physics-Informed Neural Networks (PINNS) for Geothermal Well Thermal Recovery Post-Injection
Mesias Piere Canilandi, Sutopo Sutopo, Ali Ashat, et al.
IOP Conference Series Earth and Environmental Science (2025) Vol. 1456, Iss. 1, pp. 012006-012006
Open Access
Pressure Prediction in Two-Phase Geothermal Well Flow Using Physics Informed Neural Network (PINN)
Agung Nugroho, Sutopo Sutopo, Ali Ashat
IOP Conference Series Earth and Environmental Science (2025) Vol. 1456, Iss. 1, pp. 012008-012008
Open Access
Unlocking Advanced Waste Management Models: Machine Learning Integration of Emerging Technologies into Regional Systems
Nicolás Martínez-Ramón, Ioan-Robert Istrate, Diego Iribarren, et al.
Resources Conservation & Recycling Advances (2025), pp. 200253-200253
Open Access
AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory
Guoqing Hu, Fengqi You
Applied Energy (2023) Vol. 356, pp. 122334-122334
Closed Access | Times Cited: 11
Hybrid data-mechanism-driven model of the unsteady soil temperature field for long-buried crude oil pipelines with non-isothermal batch transportation
Weixin Jiang, Junfang Wang, Petar Sabev Varbanov, et al.
Energy (2024), pp. 130354-130354
Closed Access | Times Cited: 3
PHYSICS-INFORMED NEURAL NETWORKS FOR MODELING OF 3D FLOW THERMAL PROBLEMS WITH SPARSE DOMAIN DATA
Saakaar Bhatnagar, Andrew Comerford, Araz Banaeizadeh
Journal of Machine Learning for Modeling and Computing (2024) Vol. 5, Iss. 1, pp. 39-67
Open Access | Times Cited: 2
A super-real-time three-dimension computing method of digital twins in space nuclear power
Enping Zhu, Tao Li, Jinbiao Xiong, et al.
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 417, pp. 116444-116444
Closed Access | Times Cited: 5
Physics-informed neural network for real-time thermal modeling of large-scale borehole thermal energy storage systems
Pengchao Li, Fang Guo, Yongfei Li, et al.
Energy (2024), pp. 134344-134344
Closed Access | Times Cited: 1
Fault and fracture network characterization using soft computing techniques: application to geologically complex and deeply-buried geothermal reservoirs
Qamar Yasin, Yan Ding, Qizhen Du, et al.
Geomechanics and Geophysics for Geo-Energy and Geo-Resources (2024) Vol. 10, Iss. 1
Open Access
Water-Coal Ratio Control Strategy of Ultra Supercritical Unit Based on Neural Network Inverse Model
Tian Xie, Ning He, Qiyue Xie, et al.
Mechanika (2024) Vol. 30, Iss. 4, pp. 365-370
Open Access
Supercritical Carbon Dioxide Critical Flow Model Based on a Physics-Informed Neural Network
TianSheng Chen, HaoYang Feng, Yuan Yuan, et al.
(2024)
Closed Access
Modeling unobserved geothermal structures using a physics-informed neural network with transfer learning of prior knowledge
Akihiro Shima, Kazuya Ishitsuka, Weiren Lin, et al.
Geothermal Energy (2024) Vol. 12, Iss. 1
Open Access
Supercritical Carbon Dioxide Critical Flow Model based on a Physics-informed Neural Network
TianSheng Chen, HaoYang Feng, Yuan Yuan, et al.
Energy (2024), pp. 133863-133863
Closed Access
Hydrogen Jet Diffusion Modeling of Renewable Power-to-Hydrogen System by Using Physics-Informed Graph Neural Network
Xinqi Zhang, Jihao Shi, Junjie Li, et al.
(2023)
Closed Access | Times Cited: 1
Physics Informed Neural Networks for Modeling of 3D Flow-Thermal Problems with Sparse Domain Data
Saakaar Bhatnagar, Andrew Comerford, Araz Banaeizadeh
arXiv (Cornell University) (2023)
Open Access
Stable and accurate representation of species diffusion in multilayer composite electrodes using physics-informed neural networks
Qiang Wang, Pengfei Zhang, Wei Qiu, et al.
Journal of Energy Storage (2023) Vol. 78, pp. 110016-110016
Closed Access
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