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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:

Review of multi-fidelity models
M. Giselle Fernández-Godino
arXiv (Cornell University) (2016)
Open Access | Times Cited: 113

Showing 1-25 of 113 citing articles:

A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems
Xuhui Meng, George Em Karniadakis
Journal of Computational Physics (2019) Vol. 401, pp. 109020-109020
Open Access | Times Cited: 498
Benchmark and Survey of Automated Machine Learning Frameworks
Marc-André Zöller, Marco F. Huber
Journal of Artificial Intelligence Research (2021) Vol. 70, pp. 409-472
Open Access | Times Cited: 315
The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting
Enrico Camporeale
Space Weather (2019) Vol. 17, Iss. 8, pp. 1166-1207
Open Access | Times Cited: 312
Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges
Yanwen Xu, Sara Kohtz, Jessica Boakye, et al.
Reliability Engineering & System Safety (2022) Vol. 230, pp. 108900-108900
Open Access | Times Cited: 192
Data-driven modeling for unsteady aerodynamics and aeroelasticity
Jiaqing Kou, Weiwei Zhang
Progress in Aerospace Sciences (2021) Vol. 125, pp. 100725-100725
Closed Access | Times Cited: 155
Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions
Alexander Garzón, Zoran Kapelan, Jeroen Langeveld, et al.
Water Resources Research (2022) Vol. 58, Iss. 5
Open Access | Times Cited: 85
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network
Yunyang Zhang, Zhiqiang Gong, Weien Zhou, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106354-106354
Open Access | Times Cited: 28
GP+: A Python library for kernel-based learning via Gaussian processes
Amin Yousefpour, Zahra Zanjani Foumani, Mehdi H. Shishehbor, et al.
Advances in Engineering Software (2024) Vol. 195, pp. 103686-103686
Open Access | Times Cited: 12
Deep learning-enhanced efficient seismic analysis of structures with multi-fidelity modeling strategies
De‐Cheng Feng, Shi‐Zhi Chen, Ertuǧrul Taciroğlu
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 421, pp. 116775-116775
Closed Access | Times Cited: 11
Multi-fidelity strength monitoring method for dynamic response of deep-sea pipelines based on digital-twin technology
Jianxing Yu, Zihang Jin, Yu Yang, et al.
Applied Ocean Research (2025) Vol. 154, pp. 104414-104414
Open Access | Times Cited: 1
Machine learning models for predicting ultimate bond strength of grouted sleeve connections
J. Lou, Yixuan Li, Qian Feng, et al.
Structures (2025) Vol. 72, pp. 108186-108186
Closed Access | Times Cited: 1
Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics
Manuel A. Roehrl, Thomas A. Runkler, Veronika Brandtstetter, et al.
IFAC-PapersOnLine (2020) Vol. 53, Iss. 2, pp. 9195-9200
Open Access | Times Cited: 65
Multifidelity prediction in wildfire spread simulation: Modeling, uncertainty quantification and sensitivity analysis
Mario M. Valero, Lluís Jofre, Ricardo Torres
Environmental Modelling & Software (2021) Vol. 141, pp. 105050-105050
Open Access | Times Cited: 42
Perspective: Predicting and optimizing thermal transport properties with machine learning methods
Wei Han, Hua Bao, Xiulin Ruan
Energy and AI (2022) Vol. 8, pp. 100153-100153
Open Access | Times Cited: 36
Multi-fidelity Data Aggregation using Convolutional Neural Networks
Jie Chen, Yi Gao, Yongming Liu
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 391, pp. 114490-114490
Open Access | Times Cited: 29
Hyperparameter-optimized multi-fidelity deep neural network model associated with subset simulation for structural reliability analysis
João Paulo Silva Lima, Francisco Evangelista, C. Guedes Soares
Reliability Engineering & System Safety (2023) Vol. 239, pp. 109492-109492
Closed Access | Times Cited: 22
A Combined Modeling Method for Complex Multi-Fidelity Data Fusion
Lei Tang, Feng Liu, Anping Wu, et al.
Machine Learning Science and Technology (2024) Vol. 5, Iss. 3, pp. 035071-035071
Open Access | Times Cited: 5
Flexibility Reserve in Power Systems: Definition and Stochastic Multi-Fidelity Optimization
Roohallah Khatami, Masood Parvania, Akil Narayan
IEEE Transactions on Smart Grid (2019) Vol. 11, Iss. 1, pp. 644-654
Open Access | Times Cited: 43
A multi-fidelity framework for the estimation of the turbulent Schmidt number in the simulation of atmospheric dispersion
Riccardo Longo, A. Bellemans, Marco Derudi, et al.
Building and Environment (2020) Vol. 185, pp. 107066-107066
Open Access | Times Cited: 38
Comparison of Multi-Fidelity Approaches for Military Vehicle Design
Philip Beran, Dean E. Bryson, Andrew S. Thelen, et al.
AIAA Aviation 2019 Forum (2020)
Closed Access | Times Cited: 36
Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression
Kai Cheng, Zhenzhou Lü, Ying Zhen
Computer Methods in Applied Mechanics and Engineering (2019) Vol. 349, pp. 360-377
Closed Access | Times Cited: 35
Extraction of material properties through multi-fidelity deep learning from molecular dynamics simulation
Mahmudul Islam, Md Shajedul Hoque Thakur, Satyajit Mojumder, et al.
Computational Materials Science (2020) Vol. 188, pp. 110187-110187
Open Access | Times Cited: 31
Artificial Intelligence Approaches for Energetic Materials by Design: State of the Art, Challenges, and Future Directions
Joseph B. Choi, Phong Nguyen, Oishik Sen, et al.
Propellants Explosives Pyrotechnics (2023) Vol. 48, Iss. 4
Open Access | Times Cited: 11
Prediction of melt pool geometry by fusing experimental and simulation data
Nandana Menon, Amrita Basak
International Journal of Mechanical Sciences (2023) Vol. 263, pp. 108786-108786
Closed Access | Times Cited: 11
Random Forests for Heteroscedastic Data
Hugo Bellamy, Ross D. King
Lecture notes in computer science (2025), pp. 34-49
Closed Access
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