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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:

Deep-learning of parametric partial differential equations from sparse and noisy data
Hao Xu, Dongxiao Zhang, Junsheng Zeng
Physics of Fluids (2021) Vol. 33, Iss. 3
Open Access | Times Cited: 18

Showing 18 citing articles:

Attention-enhanced neural network models for turbulence simulation
Wenhui Peng, Zelong Yuan, Jianchun Wang
Physics of Fluids (2022) Vol. 34, Iss. 2
Open Access | Times Cited: 43
Discovery of Partial Differential Equations from Highly Noisy and Sparse Data with Physics-Informed Information Criterion
Hao Xu, Junsheng Zeng, Dongxiao Zhang
Research (2023) Vol. 6
Open Access | Times Cited: 15
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
Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data
Hao Xu, Dongxiao Zhang, Nanzhe Wang
Journal of Computational Physics (2021) Vol. 445, pp. 110592-110592
Open Access | Times Cited: 30
Robust discovery of partial differential equations in complex situations
Hao Xu, Dongxiao Zhang
Physical Review Research (2021) Vol. 3, Iss. 3
Open Access | Times Cited: 23
Image features of a splashing drop on a solid surface extracted using a feedforward neural network
Jingzu Yee, Akinori Yamanaka, Yoshiyuki Tagawa
Physics of Fluids (2022) Vol. 34, Iss. 1
Open Access | Times Cited: 16
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
Identification of parametric dynamical systems using integer programming
Kazem Meidani, Amir Barati Farimani
Expert Systems with Applications (2023) Vol. 219, pp. 119622-119622
Open Access | Times Cited: 8
Temporally sparse data assimilation for the small-scale reconstruction of turbulence
Yunpeng Wang, Zelong Yuan, Chenyue Xie, et al.
Physics of Fluids (2022) Vol. 34, Iss. 6
Open Access | Times Cited: 12
Evaluation of Machine Learning Methodologies Using Simple Physics Based Conceptual Models for Flow in Porous Media
Daulet Magzymov, Ram R. Ratnakar, Birol Dindoruk, et al.
SPE Annual Technical Conference and Exhibition (2021)
Closed Access | Times Cited: 7
Bi-level Identification of Governing Equations for Nonlinear Physical Systems
Lijun Yang, Zeyu Li, Huining Yuan, et al.
Research Square (Research Square) (2024)
Open Access
The PINNs method discovery to the solution of coupled Wave- Klein-Gordon equations
Tianyi Wang, Xuebin Chi
Journal of Physics Conference Series (2021) Vol. 1754, Iss. 1, pp. 012228-012228
Open Access | Times Cited: 2
KO-PDE: Kernel Optimized Discovery of Partial Differential Equations with Varying Coefficients.
Yingtao Luo, Qiang Liu, Yuntian Chen, et al.
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 2
A Method for Computing Inverse Parametric Pde Problems with Random-Weight Neural Networks
Suchuan Dong, Yiran Wang
SSRN Electronic Journal (2022)
Open Access | Times Cited: 1
Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion
Hao Xu, Junsheng Zeng, Dongxiao Zhang
arXiv (Cornell University) (2022)
Open Access
A Bayesian approach to data-driven discovery of nonlinear dynamic equations
Joshua S. North
(2022)
Open Access
Robust discovery of partial differential equations in complex situations
Hao Xu, Dongxiao Zhang
(2021)
Closed Access
Modelling process dynamics by the discovery of partial differential equations using a data-driven and hybrid modelling approach
Kiran Raviprakash
(2021)
Closed Access
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