The DOI matches its Crossref record; 135 of the 157 references it cites were found in a registry or an index.
The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities
Open accessAbstract
Digital twinning is one of the top ten technology trends in the last couple of years, due to its high applicability in the industrial sector. The integration of big data analytics and artificial intelligence/machine learning (AI-ML) techniques with digital twinning, further enriches its significance and research potential with new opportunities and unique challenges. To date, a number of scientific models have been designed and implemented related to this evolving topic. However, there is no systematic review of digital twinning, particularly focusing on the role of AI-ML and big data, to guide the academia and industry towards future developments. Therefore, this article emphasizes the role of big data and AI-ML in the creation of digital twins (DTs) or DT-based systems for various industrial applications, by highlighting the current state-of-the-art deployments. We performed a systematic review on top of multidisciplinary electronic bibliographic databases, in addition to existing patents in the field. Also, we identified development-tools that can facilitate various levels of the digital twinning. Further, we designed a big data driven and AI-enriched reference architecture that leads developers to a complete DT-enabled system. Finally, we highlighted the research potential of AI-ML for digital twinning by unveiling challenges and current opportunities.
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“High costs of implementation due to the increased amount of sensors and computational resources needed [10,18].”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [18]
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Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
Altman, N. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475879 · Cited by 5,657
Fuzzy logic in control systems: fuzzy logic controller. I
Lee, C. C. (1990) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.52551 · Cited by 5,630
Extreme Learning Machine for Regression and Multiclass Classification
Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2168604 · Cited by 5,575
Systematic Literature Reviews in Software Engineering—A Systematic Literature Review
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009) · Information and Software Technology · doi:10.1016/j.infsof.2008.09.009 · Cited by 4,537
Flagged first, then by citations
22 of the 157 references couldn’t be matched to a known paper.
Research works printed but not linked: 17
- gulli, 2017, Deep Learning with Keras
- brockman, 2016, OpenAI gym, arXiv 1606 01540 [cs]
- foundation, 2008, Unified Architecture
- 2020, Which Technologies is Jeti Considering?
- bacchiega, 2018, Creating an embedded digital twin: Monitor, understand and predict device health failure, Inn4mech-Mechatronics Ind, 4
- piascik, 2010, Technology area 12: Materials, structures, mechanical systems, and manufacturing road map, NASA Office of the Chief Technologist
- marchal, 2016, VPH The Ultimate Stage Before Your Own Medical Digital Twin
- copley, 2018, Medical Technology Firms Develop ‘Digital Twins’ for Personalized Health Care
- holtmannspotter, 2017, Simulation in clinical practice: First experience with Sim&Cure before implantation of flow diverter (pipeline) or web-device for the treatment of intracranial aneurysm, Proc ABC/WIN
- bosch rexroth, 2010, Indramotion Mtx
- glikson, 2011, Fi-Ware: Core platform for future Internet applications, Proc the 4th Annual Int'l Conf Systems and Storage
- scharff, 2019, From Digital Twin to Improved Patient Experience
- lammers, 2018, Opinion |Digital Twin Offers Huge Opportunities for Real Estate Life Cycle
- miller, 2019, Triton actor TTP profile, custom attack tools, detections, and ATT&CK mapping, FireEye threat research blog
- cearley, 2017, Top 10 strategic technology trends for 2017: A gartner trend insight report, Gartner, 23
- cearley, 2017, Top 10 strategic technology trends for 2018, Gartner
- cearley, 2018, Top 10 strategic technology trends for 2019, Gartner
Cited by
Sorted by citations
Items 601–617 of 617
Mızrak, F., & Elbir, U. (2026) · Energies · doi:10.3390/en19174048
AI-Enabled Digital Twins for Underground UAV Communication: A Survey, Taxonomy, and Research Roadmap
Javaid, S., Fahim, H., Cheng, B., Li, G., & He, B. (2026) · IEEE Sensors Reviews · doi:10.1109/sr.2026.3728774
From island to asset: Archiving the first ex situ nation
Cnaani, O. (2026) · Cambridge Forum on AI Culture and Society · doi:10.1017/cfc.2026.10033
Hybrid-electric powertrains in aviation: modeling and simulating component dynamics
Yıldız, M., & Gokce, C. (2026) · International Journal of Turbo and Jet Engines · doi:10.1515/tjj-2026-0079
Digital Twin Technology for Marine Diesel Engines in Operation: A Review
Kopic, M., Vukelić, G., Bernečić, D., & Senčić, T. (2026) · Polish Maritime Research · doi:10.2478/pomr-2026-0037
Digital Twins for Offshore Wind Operation and Maintenance: A Review
Hu, X., Pandit, R., Zhao, Y., Namoano, B., & Hong, J. (2026) · Wind Energy · doi:10.1002/we.70153
Mulongo, N. Y. (2026) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc70543.2026.11693913
MERCAN, T., & Atalay, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7511999
Digital Twin-Based Optimization of Production Processes in Smart Factories
Hemal, M. A. K. P., Hasan, M. H., Shahnawaj, M., Hellol, H. I., Contreras, K. A. Z., & Ridwan, M. A. (2026) · Technology and Innovation Conference (TIC) · doi:10.1109/tic68483.2026.11702877
Quamar, M. M., & Nasir, A. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.116367 · Cited by 1
Cho, M., Kang, Y.-J., Choi, S., Park, N., & Noh, Y. (2026) · Journal of Building Engineering · doi:10.1016/j.jobe.2026.117440
Kuo, S. (2026) · International Journal of Sustainable Transportation · doi:10.1080/15568318.2026.2738714
Ethics, governance, and security in the age of digital twin-driven autonomous electric vehicles
Kumar, N., Mittal, D. P. A., Kumar, T., Agarwal, R., & Kumar, R. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-43962-9.00016-4
Designing evaluation rubrics for the development of virtual human twins in healthcare
Krishnamurthi, V., Edirisinghe, R. K., John, S. E., Wickramasinghe, N. S., & Plebanski, M. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag174
Design of a digital twin for an impedance-based microplastic sensor using a flow-through method
Bifano, L., Heckner, E., Müller, P., Felber, V., & Fischerauer, G. (2026) · tm - Technisches Messen · doi:10.1515/teme-2026-0078
Application of Physically Informed Neural Networks in Production Forecasting
Motaei, E., Mohammadi, H. A., & Aabdollahzadeh, A. (2026) · Abu Dhabi International Petroleum Exhibition & Conference (ADIPEC) · doi:10.2118/235665-ms
Metadata
- Authors
- Rathore, M. M., Shah, S. A., Shukla, D., Bentafat, E., & Bakiras, S.
- Affiliations
- Hamad bin Khalifa University (QA)
- University of Tartu (EE)
- University of New Brunswick (CA)
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 32030-32052
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3132985895
- Cited by
- 616
- References
- 157
- Citations by year
- 2026: 114 · 2025: 185 · 2024: 145 · 2023: 94 · 2022: 67 · 2021: 11
- FWCI
- 7.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Keywords
- digital twin, artificial intelligence, machine learning, big data analytics, industrial applications, reference architecture, patents
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