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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:

A physically consistent framework for fatigue life prediction using probabilistic physics-informed neural network
Taotao Zhou, Shan Jiang, Te Han, et al.
International Journal of Fatigue (2022) Vol. 166, pp. 107234-107234
Closed Access | Times Cited: 87

Showing 26-50 of 87 citing articles:

Multiaxial fatigue life prediction using physics-informed neural networks with sensitive features
GaoYuan He, Yongxiang Zhao, ChuLiang Yan
Engineering Fracture Mechanics (2023) Vol. 289, pp. 109456-109456
Closed Access | Times Cited: 21
A Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation
Alessandro Tognan, Andrea Patané, Luca Laurenti, et al.
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 418, pp. 116521-116521
Open Access | Times Cited: 20
A physics‐informed generative adversarial network framework for multiaxial fatigue life prediction
GaoYuan He, Yongxiang Zhao, ChuLiang Yan
Fatigue & Fracture of Engineering Materials & Structures (2023) Vol. 46, Iss. 10, pp. 4036-4052
Open Access | Times Cited: 17
A hybrid distribution characteristics of equivalent structural stress method for fatigue evaluation of welded structures
Zhe Zhang, Bing Yang, Yuedong Wang, et al.
International Journal of Fatigue (2023) Vol. 179, pp. 108057-108057
Closed Access | Times Cited: 17
Experimental investigation and phenomenological modeling of fatigue crack growth in X80 pipeline steel under random loading
Weixing Liang, Min Lou, Chen Zhang, et al.
International Journal of Fatigue (2024) Vol. 182, pp. 108169-108169
Closed Access | Times Cited: 8
Embedding physical knowledge in deep neural networks for predicting the phonon dispersion curves of cellular metamaterials
Zihan Wang, Weikang Xian, Ying Li, et al.
Computational Mechanics (2023) Vol. 72, Iss. 1, pp. 221-239
Closed Access | Times Cited: 15
Physics-informed neural network for creep-fatigue life prediction of Inconel 617 and interpretation of influencing factors
Shanglin Zhang, Lanyi Wang, Shun‐Peng Zhu, et al.
Materials & Design (2024) Vol. 245, pp. 113267-113267
Open Access | Times Cited: 6
Multi-physics information-integrated neural network for fatigue life prediction of additively manufactured Hastelloy X superalloy
Haijie Wang, Bo Li, Liming Lei, et al.
Virtual and Physical Prototyping (2024) Vol. 19, Iss. 1
Open Access | Times Cited: 5
Quantification of uncertainty in a defect-based Physics-Informed Neural Network for fatigue evaluation and insights on influencing factors
Emanuele Avoledo, Alessandro Tognan, Enrico Salvati
Engineering Fracture Mechanics (2023) Vol. 292, pp. 109595-109595
Open Access | Times Cited: 13
Uncertainty-aware fatigue-life prediction of additively manufactured Hastelloy X superalloy using a physics-informed probabilistic neural network
Haijie Wang, Bo Li, Liming Lei, et al.
Reliability Engineering & System Safety (2023) Vol. 243, pp. 109852-109852
Closed Access | Times Cited: 13
Probabilistic defect-based modelling of fatigue strength for incomplete datasets assisted by literature data
Alessandro Tognan, Enrico Salvati
International Journal of Fatigue (2023) Vol. 173, pp. 107665-107665
Open Access | Times Cited: 12
On the generalization capability of artificial neural networks used to estimate fretting fatigue life
Giorgio André Brito Oliveira, Raphael Cardoso, Raimundo Carlos Silvério Freire Júnior, et al.
Tribology International (2023) Vol. 192, pp. 109222-109222
Closed Access | Times Cited: 12
A unified estimation method for gear fatigue P-S-N curves and fatigue limits based on ensemble learning and data augmentation
Huaiju Liu, Yang Li, Zehua Lu, et al.
Engineering Fracture Mechanics (2024) Vol. 298, pp. 109941-109941
Closed Access | Times Cited: 4
A physics‐informed neural network framework based on fatigue indicator parameters for very high cycle fatigue life prediction of an additively manufactured titanium alloy
Hang Li, Guanze Sun, Tian Zhao, et al.
Fatigue & Fracture of Engineering Materials & Structures (2024)
Closed Access | Times Cited: 4
Damage mechanics coupled with a transfer learning approach for the fatigue life prediction of bronze/steel diffusion welded bimetallic material
Qianyu Xia, Chenhao Ji, Zhixin Zhan, et al.
International Journal of Fatigue (2024), pp. 108631-108631
Closed Access | Times Cited: 4
A physics-informed neural network method for identifying parameters and predicting remaining life of fatigue crack growth
Wangwang Liao, Xiangyun Long, Chao Jiang
International Journal of Fatigue (2024), pp. 108678-108678
Closed Access | Times Cited: 4
Study on high cycle fatigue behaviours and modelling of cast aluminium alloy at elevated temperatures
Yuan‐Chang Liang, Zhengxing Zuo, Jundiao Wang, et al.
Engineering Failure Analysis (2024), pp. 109031-109031
Closed Access | Times Cited: 4
Very high cycle fatigue life prediction of Ti60 alloy based on machine learning with data enhancement
Hongjiang Qian, Zhiyong Huang, Yeting Xu, et al.
Engineering Fracture Mechanics (2023) Vol. 289, pp. 109431-109431
Closed Access | Times Cited: 11
Fatigue reliability analysis of aeroengine blade-disc systems using physics-informed ensemble learning
Xueqin Li, Lu-Kai Song, Yat Sze Choy, et al.
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2023) Vol. 381, Iss. 2260
Closed Access | Times Cited: 11
Physics-constrained Gaussian process for life prediction under in-phase multiaxial cyclic loading with superposed static components
Aleksander Karolczuk, Yongming Liu, Krzysztof Kluger, et al.
International Journal of Fatigue (2023) Vol. 175, pp. 107776-107776
Open Access | Times Cited: 10
Value of process understanding in the era of machine learning: A case for recession flow prediction
Prashant Istalkar, Akshay Kadu, Basudev Biswal
Journal of Hydrology (2023) Vol. 626, pp. 130350-130350
Closed Access | Times Cited: 10
A hybrid physics-informed machine learning approach for time-dependent reliability assessment of electromagnetic relays
Fabin Mei, Hao Chen, Wenying Yang, et al.
Reliability Engineering & System Safety (2024) Vol. 252, pp. 110385-110385
Closed Access | Times Cited: 4
Barriers to Adoption of Artificial Intelligence in Metal Additive Manufacturing
Wayne E. King
(2025)
Closed Access
Machine learning model for predicting the influence of crystallographic orientation on thermomechanical fatigue of Ni-base superalloys
Rohan Acharya, Alexander N. Caputo, Richard W. Neu
International Journal of Fatigue (2025), pp. 108832-108832
Closed Access
A data-informed knowledge discovery framework to predict fatigue properties of additively manufactured Ti–6Al–4V, IN718 and AlSi10Mg alloys using fatigue databases
Samuel Onimpa Alfred, Mehdi Amiri
Progress in Additive Manufacturing (2025)
Open Access

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