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AI simulation by digital twins: systematic survey, reference framework, and mapping to a standardized architecture.

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Title: AI simulation by digital twins: systematic survey, reference framework, and mapping to a standardized architecture.
Authors: Liu, Xiaoran1 (AUTHOR) liu2706@mcmaster.ca, David, Istvan1,2 (AUTHOR) istvan.david@mcmaster.ca
Source: Software & Systems Modeling. Jun2026, Vol. 25 Issue 3, p731-756. 26p.
Subjects: Digital twin, Software architecture, Digital computer simulation, Software frameworks, Synthetic data, Intelligent agents
Abstract: Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual training environments in which AI agents can be safely and efficiently developed with simulated, synthetic data. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this article, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Based on our findings, we derive a reference framework and provide architectural guidelines by mapping it onto the ISO 23247 reference architecture for digital twins. Finally, we identify challenges and research opportunities for prospective researchers. [ABSTRACT FROM AUTHOR]
Copyright of Software & Systems Modeling is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Software+architecture%22">Software architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+computer+simulation%22">Digital computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Software+frameworks%22">Software frameworks</searchLink><br /><searchLink fieldCode="DE" term="%22Synthetic+data%22">Synthetic data</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+agents%22">Intelligent agents</searchLink>
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  Data: Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual training environments in which AI agents can be safely and efficiently developed with simulated, synthetic data. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this article, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Based on our findings, we derive a reference framework and provide architectural guidelines by mapping it onto the ISO 23247 reference architecture for digital twins. Finally, we identify challenges and research opportunities for prospective researchers. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Software & Systems Modeling is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2026
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