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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:

Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks
Guang Lin, Christian Moya, Zecheng Zhang
Engineering Applications of Artificial Intelligence (2023) Vol. 125, pp. 106689-106689
Open Access | Times Cited: 11

Showing 11 citing articles:

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics
Salah A. Faroughi, Nikhil M. Pawar, Célio Fernandes, et al.
Journal of Computing and Information Science in Engineering (2024) Vol. 24, Iss. 4
Closed Access | Times Cited: 39
A multi-fidelity deep operator network (DeepONet) for fusing simulation and monitoring data: Application to real-time settlement prediction during tunnel construction
Xu Chen, Ba Trung Cao, Yong Yuan, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108156-108156
Open Access | Times Cited: 10
D2NO: Efficient handling of heterogeneous input function spaces with distributed deep neural operators
Zecheng Zhang, Christian Moya, Lu Lu, et al.
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 428, pp. 117084-117084
Open Access | Times Cited: 5
Towards a foundation model for partial differential equations: Multioperator learning and extrapolation
Jingmin Sun, Yuxuan Liu, Zecheng Zhang, et al.
Physical review. E (2025) Vol. 111, Iss. 3
Closed Access
En-DeepONet: An enrichment approach for enhancing the expressivity of neural operators with applications to seismology
Ehsan Haghighat, Umair bin Waheed, George Em Karniadakis
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 420, pp. 116681-116681
Closed Access | Times Cited: 14
Conformalized-DeepONet: A distribution-free framework for uncertainty quantification in deep operator networks
Christian Moya, Amirhossein Mollaali, Zecheng Zhang, et al.
Physica D Nonlinear Phenomena (2024), pp. 134418-134418
Closed Access | Times Cited: 4
Chaos measure dynamics in a multifactor model for financial market predictions
Markus Vogl
Communications in Nonlinear Science and Numerical Simulation (2023) Vol. 130, pp. 107760-107760
Closed Access | Times Cited: 11
Hierarchical deep learning-based adaptive time stepping scheme for multiscale simulations
Asif Hamid, Danish Rafiq, Shahkar Ahmad Nahvi, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108430-108430
Closed Access
PREDICTING FLUID PARTICLE TRAJECTORIES WITHOUT FLOW COMPUTATIONS: A DATA-DRIVEN APPROACH
Jianchen Wei, Melissa Green, Lixin Shen, et al.
Journal of Machine Learning for Modeling and Computing (2024) Vol. 5, Iss. 2, pp. 73-111
Closed Access
Neural Dynamical Operator: Continuous Spatial-Temporal Model with Gradient-Based and Derivative-Free Optimization Methods
Chuanqi Chen, Jinlong Wu
Journal of Computational Physics (2024) Vol. 520, pp. 113480-113480
Open Access
A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients
Amirhossein Mollaali, İzzet Şahin, Iqrar Raza, et al.
Fluids (2023) Vol. 8, Iss. 12, pp. 323-323
Open Access | Times Cited: 1
Co-integrated Chaos Measure Dynamics in a Multifactor Model for Financial Market Predictions
Markus Vogl
SSRN Electronic Journal (2022)
Closed Access | Times Cited: 1
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