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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:

Surrogate modeling of parameterized multi-dimensional premixed combustion with physics-informed neural networks for rapid exploration of design space
Kai Liu, Kun Luo, Yuzhou Cheng, et al.
Combustion and Flame (2023) Vol. 258, pp. 113094-113094
Closed Access | Times Cited: 20

Showing 20 citing articles:

From PINNs to PIKANs: recent advances in physics-informed machine learning
Juan Diego Toscano, Vivek Oommen, Alan John Varghese, et al.
Machine learning for computational science and engineering (2025) Vol. 1, Iss. 1
Closed Access | Times Cited: 3
FlamePINN-1D: Physics-informed neural networks to solve forward and inverse problems of 1D laminar flames
Jiahao Wu, Su Zhang, Yuxin Wu, et al.
Combustion and Flame (2025) Vol. 273, pp. 113964-113964
Closed Access | Times Cited: 2
Unit Operation and Process Modeling with Physics-Informed Machine Learning
Haochen Li, David Spelman, John J. Sansalone
Journal of Environmental Engineering (2024) Vol. 150, Iss. 4
Open Access | Times Cited: 8
FO-PINN: A First-Order formulation for Physics-Informed Neural Networks
Rini Jasmine Gladstone, Mohammad Amin Nabian, N. Sukumar, et al.
Engineering Analysis with Boundary Elements (2025) Vol. 174, pp. 106161-106161
Open Access
Physics-informed neural networks for Kelvin–Helmholtz instability with spatiotemporal and magnitude multiscale
Jiahao Wu, Yuxin Wu, Xin Li, et al.
Physics of Fluids (2025) Vol. 37, Iss. 3
Closed Access
Advancements in combustion technologies: A review of innovations, methodologies, and practical applications
Abdellatif M. Sadeq, Raad Z. Homod, Husam Abdulrasool Hasan, et al.
Energy Conversion and Management X (2025), pp. 100964-100964
Open Access
Research on intelligent prediction method of supersonic flow field in scramjet based on deep learning: A review
Xue Deng, Ye Tian, Erda Chen, et al.
Expert Systems with Applications (2025), pp. 127500-127500
Closed Access
Operator learning for urban water clarification hydrodynamics and particulate matter transport with physics-informed neural networks
Haochen Li, Mohamed Shatarah
Water Research (2024) Vol. 251, pp. 121123-121123
Closed Access | Times Cited: 4
A deep-learning super-resolution reconstruction model of turbulent reacting flow
Zhentao Pang, Kai Liu, Hualin Xiao, et al.
Computers & Fluids (2024) Vol. 275, pp. 106249-106249
Closed Access | Times Cited: 4
Intelligent reconstruction of unsteady combustion flow field of scramjet based on physical information constraints
Xue Deng, Mingming Guo, Yi Zhang, et al.
Physics of Fluids (2024) Vol. 36, Iss. 7
Closed Access | Times Cited: 4
Hypersonic Inlet Flow Field Reconstruction Dominated by Shock Wave and Boundary Layer Based on Small Sample Physics-informed Neural Networks
Mingming Guo, Xue Deng, Yue Ma, et al.
Aerospace Science and Technology (2024) Vol. 150, pp. 109205-109205
Closed Access | Times Cited: 3
Parameterized physics-informed neural networks (P-PINNs) solution of uniform flow over an arbitrarily spinning spherical particle
Kai Liu, Kun Luo, Yuzhou Cheng, et al.
International Journal of Multiphase Flow (2024) Vol. 180, pp. 104937-104937
Closed Access | Times Cited: 3
Supersonic combustion flow field reconstruction based on multi-view domain adaptation generative network in scramjet combustor
Mingming Guo, Erda Chen, Ye Tian, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 136, pp. 108981-108981
Closed Access | Times Cited: 2
Physics-informed neural networks coupled with flamelet/progress variable model for solving combustion physics considering detailed reaction mechanism
Mengze Song, Xinzhou Tang, Jiangkuan Xing, et al.
Physics of Fluids (2024) Vol. 36, Iss. 10
Closed Access | Times Cited: 2
Interfacial conditioning in physics informed neural networks
Saykat Kumar Biswas, N. K. Anand
Physics of Fluids (2024) Vol. 36, Iss. 7
Closed Access | Times Cited: 1
Surrogate modeling of multi-dimensional premixed and non-premixed combustion using pseudo-time stepping physics-informed neural networks
Zhen Cao, Kai Liu, Kun Luo, et al.
Physics of Fluids (2024) Vol. 36, Iss. 11
Closed Access | Times Cited: 1
Parameterized physics-informed neural networks for a transient thermal problem: A pure physics-driven approach
M. Gholampour, Zahra Hashemi, Ming Chang Wu, et al.
International Communications in Heat and Mass Transfer (2024) Vol. 159, pp. 108330-108330
Closed Access | Times Cited: 1
RF-PINNs: Reactive Flow Physics-Informed Neural Networks for Field Reconstruction of Laminar and Turbulent Flames using Sparse Data
Vikas Yadav, Mario Casel, Abdulla Ghani
Journal of Computational Physics (2024), pp. 113698-113698
Open Access | Times Cited: 1
Efficient optimization design of flue deflectors through parametric surrogate modeling with physics-informed neural networks
Zhen Cao, Kai Liu, Kun Luo, et al.
Physics of Fluids (2023) Vol. 35, Iss. 12
Open Access | Times Cited: 3
Acceleration of the complex reacting flow simulation with a generalizable neural network based on meta-learning
Tianzi Bai, Ying Huai, Tingting Liu, et al.
Fuel (2024) Vol. 372, pp. 132173-132173
Closed Access
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