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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:

Data-driven stress and strain curves of the unidirectional composites by deep neural networks with principal component analysis and selective-data augmentation
Kyung Hwan Kim, Myeong‐Seok Go, Jae Hyuk Lim, et al.
Composite Structures (2023) Vol. 313, pp. 116902-116902
Closed Access | Times Cited: 21

Showing 21 citing articles:

Advanced computational modelling of composite materials
Zhengqiang Cheng, Hu Liu, Wei Tan
Engineering Fracture Mechanics (2024) Vol. 305, pp. 110120-110120
Open Access | Times Cited: 16
Artificial neural network-based temperature prediction of a lunar orbiter in thermal vacuum test: Data-driven reduced-order models
Byungkwan Jang, Woojin Lee, Jang-Joon Lee, et al.
Aerospace Science and Technology (2024) Vol. 145, pp. 108867-108867
Closed Access | Times Cited: 10
Peridynamics-fueled convolutional neural network for predicting mechanical constitutive behaviors of fiber reinforced composites
Binbin Yin, Jiasheng Huang, Weikang Sun
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 431, pp. 117309-117309
Closed Access | Times Cited: 10
Recurrent neural network (RNN) and long short-term memory neural network (LSTM) based data-driven methods for identifying cohesive zone law parameters of nickel-modified carbon nanotube reinforced sintered nano-silver adhesives
Yanwei Dai, Chuncheng Wei, Fei Qin
Materials Today Communications (2024) Vol. 39, pp. 108991-108991
Closed Access | Times Cited: 9
A review on recent applications of machine learning in mechanical properties of composites
Yi Liang, Xinyue Wei, Yongyue Peng, et al.
Polymer Composites (2024)
Closed Access | Times Cited: 6
Filled Elastomers: Mechanistic and Physics-Driven Modeling and Applications as Smart Materials
Weikang Xian, You-Shu Zhan, Amitesh Maiti, et al.
Polymers (2024) Vol. 16, Iss. 10, pp. 1387-1387
Open Access | Times Cited: 4
Enhancing Prediction Performance and Generalizing for Transverse Behavior of Unidirectional Composites via Strategic Input Feature Augmentation
Minwoo Park, Jiyoung Jung, Hyeonbin Moon, et al.
Advanced Theory and Simulations (2025)
Closed Access
Deep learning without stress data on the discovery of multi-regional hyperelastic properties
Ruike Shi, Haitian Yang, Jianxu Chen, et al.
Computational Mechanics (2025)
Closed Access
Data generation and deep neural network predictions for aged mechanical properties
Enio H. P. Da Silva, Sílvio de Barros, Pascal Casari, et al.
Polymer Engineering and Science (2025)
Closed Access
Crack-Net: A Deep Learning Approach to Predict Crack Propagation and Stress–Strain Curves in Particulate Composites
Hao Xu, Wei Fan, Lecheng Ruan, et al.
Engineering (2025)
Open Access
Deep learning approach for predicting multi-component stress fields in fiber-reinforced composites under different load paths
Xiang Peng, Qin Yao, Bing Yi, et al.
Composites Science and Technology (2025), pp. 111198-111198
Closed Access
Credal identification of damage patterns in ultra-thin-ply composite bonded/bolted interference-fit joints
Yonggang Kang, Shuaijia Kou, Ke‐Juan Meng, et al.
Engineering Failure Analysis (2024) Vol. 162, pp. 108371-108371
Closed Access | Times Cited: 2
Prediction of Macroscopic Compressive Mechanical Properties for 2.5D Woven Composites Based on Artificial Neural Network
Jie Zhou, Haolin Wei, Zhen Wu, et al.
Fibers and Polymers (2024) Vol. 25, Iss. 8, pp. 3185-3199
Closed Access | Times Cited: 1
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 | Times Cited: 1
Expanding Design Spaces in Digital Composite Materials: A Multi‐Input Deep Learning Approach Enhanced by Transfer Learning and Multi‐kernel Network
Donggeun Park, Minwoo Park, Seunghwa Ryu
Advanced Theory and Simulations (2023) Vol. 6, Iss. 11
Open Access | Times Cited: 2
Surrogate modeling of the fan plot of a rotor system considering composite blades using convolutional neural networks with image composition
Hong‐Kyun Noh, Jae Hyuk Lim, Seung‐Chul Lee, et al.
Journal of Computational Design and Engineering (2023) Vol. 10, Iss. 3, pp. 1250-1266
Open Access | Times Cited: 1
Enhanced prediction of transverse mechanical behavior of unidirectional fiber-reinforced composites using new spatial descriptors based on deep neural networks
Sun Won Kim, Jae Hyuk Lim, Seong Su Kim
Composite Structures (2023) Vol. 330, pp. 117870-117870
Closed Access | Times Cited: 1
A Data-Driven Machine Learning Model for the Stress-Strain Behavior of Single Grain SAC305 Solder Joints
Debabrata Mondal, Jeffrey C. Suhling, Elham Mirkoohi, et al.
(2024), pp. 670-677
Closed Access
A comprehensive investigation on the performance of reconstruction of noncircular fiber-representative volume elements in unidirectional composites using diffusion generative models
Seong-Won Jin, Hong‐Kyun Noh, Myeong‐Seok Go, et al.
Computational Materials Science (2024) Vol. 246, pp. 113441-113441
Closed Access
Prediction of the Mechanical Responses of Single Grain Lead-Free Solder Joints Using Machine Learning
Debabrata Mondal, Jeffrey C. Suhling, Elham Mirkoohi, et al.
(2024), pp. 1-8
Closed Access
Neural network-driven framework for efficient microstructural modeling of particle-enriched composites
Shishir Barai, Feihong Liu, Manik Kumar, et al.
Materials Today Communications (2024), pp. 111278-111278
Closed Access
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