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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:

An improved data-free surrogate model for solving partial differential equations using deep neural networks
Xinhai Chen, Rongliang Chen, Qian Wan, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

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: 26
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 | Times Cited: 1
MGNet: a novel differential mesh generation method based on unsupervised neural networks
Xinhai Chen, Tiejun Li, Qian Wan, et al.
Engineering With Computers (2022) Vol. 38, Iss. 5, pp. 4409-4421
Closed Access | Times Cited: 29
Neural Operator-Based Surrogate Solver for Free-Form Electromagnetic Inverse Design
Yannick Augenstein, Taavi Repän, Carsten Rockstuhl
ACS Photonics (2023) Vol. 10, Iss. 5, pp. 1547-1557
Open Access | Times Cited: 21
An artificial viscosity augmented physics-informed neural network for incompressible flow
Yichuan He, Zhicheng Wang, Hui Xiang, et al.
Applied Mathematics and Mechanics (2023) Vol. 44, Iss. 7, pp. 1101-1110
Open Access | Times Cited: 19
Potential of physics-informed neural networks for solving fluid flow problems with parametric boundary conditions
Finn Lorenzen, Amin Zargaran, Uwe Janoske
Physics of Fluids (2024) Vol. 36, Iss. 3
Open Access | Times Cited: 5
Developing a novel structured mesh generation method based on deep neural networks
Xinhai Chen, Jie Liu, Qingyang Zhang, et al.
Physics of Fluids (2023) Vol. 35, Iss. 9
Closed Access | Times Cited: 12
Physics-informed neural network based on control volumes for solving time-independent problems
Chang Wei, Y. Fan, Yongqing Zhou, et al.
Physics of Fluids (2025) Vol. 37, Iss. 3
Closed Access
Deep learning applications in radiative magnetohydrodynamic bioconvection within a vertical wavy porous structure
S. Sarthak, D. Srinivasacharya
Physics of Fluids (2025) Vol. 37, Iss. 4
Closed Access
A network model for handling boundary conditions in stochastic partial differential equations
Jian Wang, Qiang Zhao, Witold Pedrycz, et al.
Computer Methods in Applied Mechanics and Engineering (2025) Vol. 441, pp. 117953-117953
Closed Access
A novel neural network approach for airfoil mesh quality evaluation
Xinhai Chen, Chunye Gong, Jie Liu, et al.
Journal of Parallel and Distributed Computing (2022) Vol. 164, pp. 123-132
Closed Access | Times Cited: 14
Isogeometric neural networks: A new deep learning approach for solving parameterized partial differential equations
Joshua Gasick, Xiaoping Qian
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 405, pp. 115839-115839
Open Access | Times Cited: 14
Solving seepage equation using physics-informed residual network without labeled data
Shuaijun Lv, Daolun Li, Wenshu Zha, et al.
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 418, pp. 116563-116563
Closed Access | Times Cited: 8
Deep convolutional surrogates and freedom in thermal design
Hadi Keramati, Feridun Hamdullahpur
Energy and AI (2023) Vol. 13, pp. 100248-100248
Open Access | Times Cited: 7
Developing an advanced neural network and physics solver coupled framework for accelerating flow field simulations
Xinhai Chen, Tiejun Li, Yunbo Wan, et al.
Engineering With Computers (2023) Vol. 40, Iss. 2, pp. 1111-1126
Closed Access | Times Cited: 7
Evaluating single multiplicative neuron models in physics-informed neural networks for differential equations
Melih Ağraz
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Towards a new paradigm in intelligence-driven computational fluid dynamics simulations
Xinhai Chen, Zhichao Wang, Liang Deng, et al.
Engineering Applications of Computational Fluid Mechanics (2024) Vol. 18, Iss. 1
Open Access | Times Cited: 2
Surrogate modelling of a detailed farm‐level model using deep learning
Linmei Shang, Jifeng Wang, David Schäfer, et al.
Journal of Agricultural Economics (2023) Vol. 75, Iss. 1, pp. 235-260
Open Access | Times Cited: 6
Data-driven bond-based peridynamics with nonlocal influence function for crack propagation
Jian-Xiang Ma, Xiaoping Zhou
Engineering Fracture Mechanics (2022) Vol. 272, pp. 108681-108681
Closed Access | Times Cited: 10
Using physics-informed neural networks to compute quasinormal modes
Alan S. Cornell, Anele Ncube, Gerhard Harmsen
Physical review. D/Physical review. D. (2022) Vol. 106, Iss. 12
Open Access | Times Cited: 7
A surrogate evolutionary neural architecture search algorithm for graph neural networks
Yang Liu, Jing Liu
Applied Soft Computing (2023) Vol. 144, pp. 110485-110485
Closed Access | Times Cited: 4
Modeling Transient Mixed Flows in Sewer Systems with Data Fusion via Physics-Informed Machine Learning
Shixun Li, Wenchong Tian, Hexiang Yan, et al.
Water Research X (2024) Vol. 25, pp. 100266-100266
Open Access | Times Cited: 1
Assessing physics-informed neural network performance with sparse noisy velocity data
Adhika Satyadharma, Ming‐Jyh Chern, Hirofumi Kan, et al.
Physics of Fluids (2024) Vol. 36, Iss. 10
Closed Access | Times Cited: 1
A rapid-convergent particle swarm optimization approach for multiscale design of high-permeance seawater reverse osmosis systems
Kexin Chen, Jiu Luo, Junzhi Chen, et al.
Communications Engineering (2024) Vol. 3, Iss. 1
Open Access | Times Cited: 1
How Can We Use Mathematical Modeling of Amyloid-β in Alzheimer’s Disease Research and Clinical Practices?
Chenyin Chu, Yi Ling Low, Liwei Ma, et al.
Journal of Alzheimer s Disease (2023) Vol. 97, Iss. 1, pp. 89-100
Closed Access | Times Cited: 1
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