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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:

Deep Learning Techniques for Predicting Stress Fields in Composite Materials: A Superior Alternative to Finite Element Analysis
Yasin Shokrollahi, Matthew M. Nikahd, Kimia Gholami, et al.
Journal of Composites Science (2023) Vol. 7, Iss. 8, pp. 311-311
Open Access | Times Cited: 11

Showing 11 citing articles:

Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review
Dipjyoti Nath, Ankit, Debanga Raj Neog, et al.
Archives of Computational Methods in Engineering (2024) Vol. 31, Iss. 5, pp. 2945-2984
Closed Access | Times Cited: 24
Application of Machine Learning for Real-Time Structural Integrity Assessment of Bridges
Sanduni Jayasinghe, Mojtaba Mahmoodian, Azadeh Alavi, et al.
CivilEng (2025) Vol. 6, Iss. 1, pp. 2-2
Open Access | Times Cited: 2
Applications of artificial intelligence/machine learning to high-performance composites
Yifeng Wang, Wang Kan, Chuck Zhang
Composites Part B Engineering (2024) Vol. 285, pp. 111740-111740
Closed Access | Times Cited: 12
Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites
Jayanta Bhusan Deb, Jihua Gou, Haonan Song, et al.
Journal of Composites Science (2024) Vol. 8, Iss. 3, pp. 96-96
Open Access | Times Cited: 5
A review on the applications of artificial neural network techniques for accelerating finite element analysis in the civil engineering domain
Sanduni Jayasinghe, Mojtaba Mahmoodian, Azadeh Alavi, et al.
Computers & Structures (2025) Vol. 310, pp. 107698-107698
Open Access
Deep Learning-Based Prediction of Stress and Strain Maps in Arterial Walls for Improved Cardiovascular Risk Assessment
Yasin Shokrollahi, Pengfei Dong, Changchun Zhou, et al.
Applied Sciences (2023) Vol. 14, Iss. 1, pp. 379-379
Open Access | Times Cited: 2
Predicting Mechanical Properties from Microstructure Images in Fiber-Reinforced Polymers Using Convolutional Neural Networks
Yixuan Sun, Imad Hanhan, Michael D. Sangid, et al.
Journal of Composites Science (2024) Vol. 8, Iss. 10, pp. 387-387
Open Access
Application of convolutional neural networks and ensemble methods in the fiber volume content analysis of natural fiber composites
Florian Rothenhäusler, Rodrigo Q. Albuquerque, Marcel Sticher, et al.
Machine Learning with Applications (2024), pp. 100609-100609
Open Access
A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: efficient mapping of concrete microstructures to its fracture properties
Rasoul Najafi Koopas, Shahed Rezaei, Natalie Rauter, et al.
Engineering Fracture Mechanics (2024), pp. 110675-110675
Closed Access
Deep Learning-based Prediction of Stress and Strain Maps in Arterial Walls for Improved Cardiovascular Risk Assessment
Yasin Shokrollahi, Pengfei Dong, Xianqi Li, et al.
(2023)
Open Access | Times Cited: 1
Creating semi-Quanta multi-layer synthetic CNT images using CycleGAN
Kaveh Safavigerdini, Ramakrishna Surya, Andrew Reinhard, et al.
(2023), pp. 1-6
Closed Access | Times Cited: 1
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