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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:

Physics-informed machine learning and its structural integrity applications: state of the art
Shun‐Peng Zhu, Lanyi Wang, Changqi Luo, et al.
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2023) Vol. 381, Iss. 2260
Closed Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications
Haoteng Hu, Lehua Qi, Xujiang Chao
Thin-Walled Structures (2024) Vol. 205, pp. 112495-112495
Closed Access | Times Cited: 19
Interpretable Predicting Creep Rupture Life of Superalloys: Enhanced by Domain‐Specific Knowledge
Jiawei Yin, Ziyuan Rao, Dayong Wu, et al.
Advanced Science (2024) Vol. 11, Iss. 11
Open Access | Times Cited: 11
Evaluating fatigue onset in metallic materials: Problem, current focus and future perspectives
Enrico Salvati
International Journal of Fatigue (2024) Vol. 188, pp. 108487-108487
Open Access | Times Cited: 11
Neural network integrated with symbolic regression for multiaxial fatigue life prediction
Peng Zhang, Keke Tang, Anbin Wang, et al.
International Journal of Fatigue (2024) Vol. 188, pp. 108535-108535
Closed Access | Times Cited: 9
A fatigue life prediction approach for porosity defect-induced failures in directed energy deposited Ti-6Al-4V considering crack growth environment
Dingcheng Tang, Xiaofan He, Bin Wu, et al.
International Journal of Fatigue (2024) Vol. 184, pp. 108272-108272
Closed Access | Times Cited: 7
Interpretation of fatigue lifetime prediction by machine learning modeling in piston aluminum alloys under different manufacturing and loading conditions
Mohammad Azadi, Mahmood Matin
Frattura ed Integrità Strutturale (2024) Vol. 18, Iss. 68, pp. 357-370
Open Access | Times Cited: 6
Very high-cycle fatigue behavior of steel in hydrogen environment: State of the art review and challenges
Shiyuan Yang, Abílio M.P. De Jesus, Debiao Meng, et al.
Engineering Failure Analysis (2024), pp. 108898-108898
Closed Access | Times Cited: 5
Physics-based neural networks for the characterization and behavior assessment of construction materials
Ahed Habib, M. Talha Junaid, Salah Altoubat, et al.
Journal of Building Engineering (2025) Vol. 100, pp. 111788-111788
Closed Access
Developing New Design Procedure for Bridge Construction Equipment Based on Advanced Structural Analysis
Shaoxiong Jiang, Faham Tahmasebinia
Applied Sciences (2025) Vol. 15, Iss. 5, pp. 2860-2860
Open Access
Supervised Machine Learning Models for Mechanical Properties Prediction in Additively Manufactured Composites
Darío Prada-Parra, Guilherme Rezende Bessa Ferreira, Jorge Guillermo Díaz-Rodríguez, et al.
Applied Sciences (2024) Vol. 14, Iss. 16, pp. 7009-7009
Open Access | Times Cited: 4
A systematic and bibliometric review on physics-based neural networks applications as a solution for structural engineering partial differential equations
Ahed Habib, Ausamah AL Houri, M. Talha Junaid, et al.
Structures (2024) Vol. 69, pp. 107361-107361
Closed Access | Times Cited: 4
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: 3
Physics-informed machine learning framework for creep-fatigue life prediction of a Ni-based superalloy using ensemble learning
Xi Deng, Shun‐Peng Zhu, Shanglin Zhang, et al.
Materials Today Communications (2024) Vol. 41, pp. 110260-110260
Closed Access | Times Cited: 3
Critical physics informed fatigue life prediction of laser 3D printed AlSi10Mg alloys with mass internal defects
Yanan Hu, Yufeng She, Shengchuan Wu, et al.
International Journal of Mechanical Sciences (2024), pp. 109730-109730
Closed Access | Times Cited: 3
Intelligent fatigue damage tracking and prognostics of composite structures utilizing raw images via interpretable deep learning
Panagiotis Komninos, A.E.C. Verraest, Nick Eleftheroglou, et al.
Composites Part B Engineering (2024), pp. 111863-111863
Open Access | Times Cited: 3
Prediction of multiaxial fatigue life with a data-driven knowledge transfer model
Lei Gan, Zhi‐Ming Fan, Hao Wu, et al.
International Journal of Fatigue (2024), pp. 108636-108636
Closed Access | Times Cited: 3
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: 2
Machine learning based prediction models for uniaxial ratchetting of extruded AZ31 magnesium alloy
Xiaowen Deng, Yanan Hu, Binghui Hu, et al.
Extreme Mechanics Letters (2024) Vol. 70, pp. 102193-102193
Closed Access | Times Cited: 2
Physics-informed machine learning for system reliability analysis and design with partially observed information
Yanwen Xu, Parth Bansal, Pingfeng Wang, et al.
Reliability Engineering & System Safety (2024), pp. 110598-110598
Open Access | Times Cited: 2
Data-driven probabilistic failure assessment curve based on similitude principle
Siyuan Li, Baoming Gong, Lianshuang Dai, et al.
International Journal of Solids and Structures (2024) Vol. 295, pp. 112819-112819
Closed Access | Times Cited: 1
Physics-guided neural network for fatigue life prediction of FCC-based multi-principal element alloys
Jingli Ren, Xiao Lu
Scripta Materialia (2024) Vol. 253, pp. 116307-116307
Closed Access | Times Cited: 1
Investigation Study of Structure Real Load Spectra Acquisition and Fatigue Life Prediction Based on the Optimized Efficient Hinging Hyperplane Neural Network Model
Lin Zhu, Baixi Xing, Xingbao Li, et al.
Chinese Journal of Mechanical Engineering (2024) Vol. 37, Iss. 1
Open Access | Times Cited: 1
Editorial: Advanced technologies for smart buildings and infrastructure (Part 1)
José A.F.O. Correia
Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction (2024) Vol. 177, Iss. 1, pp. 1-2
Open Access
Reliability Analysis and Machine Learning Prediction for a Repairable Load-Sharing K-Out-of-N + W: G Power Supply System
Shan Gao
IEEE Access (2024) Vol. 12, pp. 133183-133198
Open Access
Modeling of zirconium alloy cladding corrosion behavior based on neural ordinary differential equation
Tao Zhang, Yongjun Jiao, Zhenhai Liu, et al.
Nuclear Engineering and Technology (2024) Vol. 57, Iss. 3, pp. 103251-103251
Open Access
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