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 neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems
Kazuma Kobayashi, Syed Bahauddin Alam
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 8

Showing 8 citing articles:

Differentiable Deep Learning Surrogate Models Applied to the Optimization of the IFMIF-DONES Facility
Giovanni E. Romero, Guillermo Rodríguez-Llorente, L. Pacheco Rodriguez, et al.
Particles (2025) Vol. 8, Iss. 1, pp. 21-21
Open Access

Exploration of deep operator networks for predicting the piezoionic effect
Shuyu Wang, Dingli Zhang, A.H.-J. Wang, et al.
The Journal of Chemical Physics (2025) Vol. 162, Iss. 11
Closed Access

AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology
Kazuma Kobayashi, Dinesh Kumar, Syed Bahauddin Alam
Progress in Nuclear Energy (2024) Vol. 172, pp. 105177-105177
Open Access | Times Cited: 2

Enhancing thermo-hydraulic performance in dimpled channels with wavy tape inserts for heat pipe & heat exchanger design with complex energy systems
Farid Ahmed, Md. Hasan Nasrullah, Istiak Ahmad, et al.
Case Studies in Thermal Engineering (2024) Vol. 60, pp. 104583-104583
Open Access | Times Cited: 2

Development of Whole System Digital Twins for Advanced Reactors: Leveraging Graph Neural Networks and SAM Simulations
Yang Liu, Farah Alsafadi, Travis Mui, et al.
Nuclear Technology (2024), pp. 1-18
Closed Access | Times Cited: 2

Synergistic Integration of Digital Twins and Neural Networks for Advancing Optimization in the Construction Industry: A Comprehensive Review
Alexey Borovkov, Khristina Maksudovna Vafaeva, Nikolai Vatin, et al.
Construction Materials and Products (2024) Vol. 7, Iss. 4, pp. 7-7
Closed Access | Times Cited: 2

Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction
James Daniell, Kazuma Kobayashi, Ayodeji Babatunde Alajo, et al.
Energy and AI (2024), pp. 100450-100450
Open Access | Times Cited: 1

Research on Digital Twin Dynamic Modeling Method for Transmission Line Deicing System
Qinghao Chen, Tianyu Liu, Zhangqi Wang, et al.
Energies (2024) Vol. 17, Iss. 6, pp. 1424-1424
Open Access | Times Cited: 1

Non-Intrusive Uncertainty Quantification for U3Si2 and UO2 Fuels with SiC/SiC Cladding using BISON for Digital Twin-Enabling Technology
Kazuma Kobayashi, Dinesh Kumar, Susmita Naskar, et al.
arXiv (Cornell University) (2022)
Open Access | Times Cited: 2

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