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Artificial intelligence in digital twins—A systematic literature review
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0002-0813-9555
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0002-4632-4815
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0002-0870-0330
Number of Authors: 32024 (English)In: Data & Knowledge Engineering, ISSN 0169-023X, E-ISSN 1872-6933, Vol. 151, article id 102304Article, review/survey (Refereed) Published
Abstract [en]

Artificial intelligence and digital twins have become more popular in recent years and have seen usage across different application domains for various scenarios. This study reviews the literature at the intersection of the two fields, where digital twins integrate an artificial intelligence component. We follow a systematic literature review approach, analyzing a total of 149 related studies. In the assessed literature, a variety of problems are approached with an artificial intelligence-integrated digital twin, demonstrating its applicability across different fields. Our findings indicate that there is a lack of in-depth modeling approaches regarding the digital twin, while many articles focus on the implementation and testing of the artificial intelligence component. The majority of publications do not demonstrate a virtual-to-physical connection between the digital twin and the real-world system. Further, only a small portion of studies base their digital twin on real-time data from a physical system, implementing a physical-to-virtual connection.

Place, publisher, year, edition, pages
2024. Vol. 151, article id 102304
Keywords [en]
Artificial intelligence, Digital twin, Machine learning, Literature review, Business intelligence, Data mining
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:su:diva-231188DOI: 10.1016/j.datak.2024.102304ISI: 001234973200001Scopus ID: 2-s2.0-85190070128OAI: oai:DiVA.org:su-231188DiVA, id: diva2:1876183
Available from: 2024-06-24 Created: 2024-06-24 Last updated: 2024-06-24Bibliographically approved

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Kreuzer, TimPapapetrou, PanagiotisZdravkovic, Jelena

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