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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:

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
Apostolos F. Psaros, Xuhui Meng, Zongren Zou, et al.
Journal of Computational Physics (2023) Vol. 477, pp. 111902-111902
Open Access | Times Cited: 214

Showing 26-50 of 214 citing articles:

Bayesian Deep Operator Learning for Homogenized to Fine-Scale Maps for Multiscale PDE
Zecheng Zhang, Christian Moya, Wing Tat Leung, et al.
Multiscale Modeling and Simulation (2024) Vol. 22, Iss. 3, pp. 956-972
Open Access | Times Cited: 8
PINN surrogate of Li-ion battery models for parameter inference, Part II: Regularization and application of the pseudo-2D model
Malik Hassanaly, Peter J. Weddle, Ryan King, et al.
Journal of Energy Storage (2024) Vol. 98, pp. 113104-113104
Open Access | Times Cited: 8
Leveraging Viscous Hamilton–Jacobi PDEs for Uncertainty Quantification in Scientific Machine Learning
Zongren Zou, Tingwei Meng, Paula Chen, et al.
SIAM/ASA Journal on Uncertainty Quantification (2024) Vol. 12, Iss. 4, pp. 1165-1191
Open Access | Times Cited: 8
Uncertainty quantification for noisy inputs–outputs in physics-informed neural networks and neural operators
Zongren Zou, Xuhui Meng, George Em Karniadakis
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 433, pp. 117479-117479
Closed Access | Times Cited: 8
Uncertainty quantification in autoencoders predictions: Applications in aerodynamics
Ettore Saetta, Renato Tognaccini, Gianluca Iaccarino
Journal of Computational Physics (2024) Vol. 506, pp. 112951-112951
Open Access | Times Cited: 7
Predicting rainfall runoff in Southern Nigeria using a fused hybrid deep learning ensemble
Arnold Adimabua Ojugo, Patrick Ogholuwarami Ejeh, Christopher Chukwufunaya Odiakaose, et al.
International Journal of Informatics and Communication Technology (IJ-ICT) (2024) Vol. 13, Iss. 1, pp. 108-108
Open Access | Times Cited: 7
Forecasting air transportation demand and its impacts on energy consumption and emission
Majid Emami Javanmard, Yili Tang, J. Adrián Martínez-Hernández
Applied Energy (2024) Vol. 364, pp. 123031-123031
Open Access | Times Cited: 6
IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning
Ling Guo, Hao Wu, Yan Wang, et al.
Journal of Computational Physics (2024) Vol. 510, pp. 113089-113089
Open Access | Times Cited: 6
Uncertainty quantification for molecular property predictions with graph neural architecture search
Shengli Jiang, Shiyi Qin, Reid C. Van Lehn, et al.
Digital Discovery (2024) Vol. 3, Iss. 8, pp. 1534-1553
Open Access | Times Cited: 6
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: 6
Solution of physics-based Bayesian inverse problems with deep generative priors
Dhruv Patel, Deep Ray, Assad A. Oberai
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 400, pp. 115428-115428
Open Access | Times Cited: 26
Scalable uncertainty quantification for deep operator networks using randomized priors
Yi-Bo Yang, Georgios Kissas, Paris Perdikaris
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 399, pp. 115399-115399
Open Access | Times Cited: 23
BelNet: basis enhanced learning, a mesh-free neural operator
Zecheng Zhang, Leung Wing Tat, Hayden Schaeffer
Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences (2023) Vol. 479, Iss. 2276
Open Access | Times Cited: 16
A generative modeling framework for inferring families of biomechanical constitutive laws in data-sparse regimes
Minglang Yin, Zongren Zou, Enrui Zhang, et al.
Journal of the Mechanics and Physics of Solids (2023) Vol. 181, pp. 105424-105424
Open Access | Times Cited: 15
Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems
Sarah Perez, Suryanarayana Maddu, Ivo F. Sbalzarini, et al.
Journal of Computational Physics (2023) Vol. 491, pp. 112342-112342
Open Access | Times Cited: 14
Bayesian Physics-Informed Neural Networks for the Subsurface Tomography Based on the Eikonal Equation
Rongxi Gou, Yijie Zhang, Xueyu Zhu, et al.
IEEE Transactions on Geoscience and Remote Sensing (2023) Vol. 61, pp. 1-12
Open Access | Times Cited: 13
Quality measures for the evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems
Stephen Guth, Alireza Mojahed, Themistoklis P. Sapsis
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 420, pp. 116760-116760
Closed Access | Times Cited: 5
An Analysis of the Ingredients for Learning Interpretable Symbolic Regression Models with Human-in-the-loop and Genetic Programming
Giorgia Nadizar, Luigi Rovito, Andrea De Lorenzo, et al.
ACM Transactions on Evolutionary Learning and Optimization (2024) Vol. 4, Iss. 1, pp. 1-30
Closed Access | Times Cited: 5
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
Slow Invariant Manifolds of Singularly Perturbed Systems via Physics-Informed Machine Learning
Dimitrios G. Patsatzis, Gianluca Fabiani, Lucia Russo, et al.
SIAM Journal on Scientific Computing (2024) Vol. 46, Iss. 4, pp. C297-C322
Open Access | Times Cited: 5
Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification
Himanshu Sharma, Lukăš Novák, Michael D. Shields
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 431, pp. 117314-117314
Open Access | Times Cited: 5
NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements
Khemraj Shukla, Zongren Zou, Chi Hin Chan, et al.
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 433, pp. 117498-117498
Closed Access | Times Cited: 5
Machine learning based eddy current testing: A review
Nauman Munir, Jingyuan Huang, Chak‐Nam Wong, et al.
Results in Engineering (2024) Vol. 25, pp. 103724-103724
Closed Access | Times Cited: 5
Challenges in data-driven geospatial modeling for environmental research and practice
Diana Koldasbayeva, Polina Tregubova, Mikhail Gasanov, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 5
Physics-informed variational inference for uncertainty quantification of stochastic differential equations
Hyomin Shin, Minseok Choi
Journal of Computational Physics (2023) Vol. 487, pp. 112183-112183
Closed Access | Times Cited: 12

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