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The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities
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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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Issa, R., Badr, M. M., Shalash, O., Othman, A. A., Hamdan, E., Hamad, M. S., Abdel-Khalik, A. S., Ahmed, S., & Imam, S. M. (2023) · Batteries · doi:10.3390/batteries9100521 · Cited by 50
A review on digital twin application in photovoltaic energy systems: challenges and opportunities
Alao, K. T., Gilani, S. I. U.-H., Sopian, K., & Alao, T. O. (2024) · JMST Advances · doi:10.1007/s42791-024-00083-z · Cited by 56
Ranawaka, A. S. R. A., Alahakoon, D., Sun, Y., & Hewapathirana, K. (2024) · Energies · doi:10.3390/en17215342 · Cited by 57
Khan, S., Alzaabi, A., Iqbal, Z., Ratnarajah, T., & Arslan, T. (2023) · IEEE Access · doi:10.1109/access.2023.3316508 · Cited by 55
Structural health monitoring of timber bridges – A review
Abdoli, F., Rashidi, M., Wang, J., Siddique, R., & Nasir, V. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103084 · Cited by 44
Agricultural digital twin for smart farming: A review
Subeesh, A., & Chauhan, N. (2025) · Green Technologies and Sustainability · doi:10.1016/j.grets.2025.100299 · Cited by 63
Digital twins and their use in future power systems
Pálenský, P., Cvetković, M., Gusain, D., & Joseph, A. (2021) · Digital Twin · doi:10.12688/digitaltwin.17435.1 · Cited by 128
Economic impact of digital twins on renewable energy investments
Bassey, K. E., Opoku-Boateng, J., Antwi, B. O., & Ntiakoh, A. (2024) · Engineering Science & Technology Journal · doi:10.51594/estj.v5i7.1318 · Cited by 42
Data Integration for Digital Twins in Industrial Automation: A Systematic Literature Review
Hildebrandt, G., Dittler, D., Habiger, P., Drath, R., & Weyrich, M. (2024) · IEEE Access · doi:10.1109/access.2024.3465632 · Cited by 44
Onile, A. E., Petlenkov, E., Levron, Y., & Belikov, J. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.03.018 · Cited by 47
Data-Driven Digital Twin Framework for Predictive Maintenance of Smart Manufacturing Systems
Khan, T. M. Y., Khan, U., Khan, A., Mollan, C., Morkvėnaitė-Vilkončienė, I., & Pandey, V. (2025) · Machines · doi:10.3390/machines13060481 · Cited by 40
Sharotry, A., Jimenez, J. A., Mediavilla, F. A. M., Wierschem, D. C., Koldenhoven, R. M., & Valles, D. (2022) · IEEE Access · doi:10.1109/access.2022.3145984 · Cited by 39
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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