Model‐Driven Engineering for Digital Twins: Opportunities and Challenges.

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Title: Model‐Driven Engineering for Digital Twins: Opportunities and Challenges.
Authors: Michael, Judith1 (AUTHOR) michael@se-rwth.de, Cleophas, Loek2,3 (AUTHOR), Zschaler, Steffen4 (AUTHOR), Clark, Tony5 (AUTHOR), Combemale, Benoit6 (AUTHOR), Godfrey, Thomas4 (AUTHOR), Khelladi, Djamel Eddine7 (AUTHOR), Kulkarni, Vinay8 (AUTHOR), Lehner, Daniel9 (AUTHOR), Rumpe, Bernhard1 (AUTHOR), Wimmer, Manuel9 (AUTHOR), Wortmann, Andreas10 (AUTHOR), Ali, Shaukat11 (AUTHOR), Barn, Balbir12 (AUTHOR), Barosan, Ion2 (AUTHOR), Bencomo, Nelly13 (AUTHOR), Bordeleau, Francis14 (AUTHOR), Grossmann, Georg15 (AUTHOR), Karsai, Gabor16 (AUTHOR), Kopp, Oliver10 (AUTHOR)
Source: Systems Engineering. Sep2025, Vol. 28 Issue 5, p659-670. 12p.
Subjects: Digital twin, Model-driven software architecture, Systems engineering, Data pipelining, Computer simulation, Simulation methods & models
Abstract: Digital twins are increasingly used across a wide range of industries. Modeling is a key to digital twin development—both when considering the models which a digital twin maintains of its real‐world complement ("models in digital twin") and when considering models of the digital twin as a complex (software) system itself. Thus, systematic development and maintenance of these models is a key factor in effective and efficient digital twin development, maintenance, and use. We argue that model‐driven engineering (MDE), a field with almost three decades of research, will be essential for improving the efficiency and reliability of future digital twin development. To do so, we present an overview of the digital twin life cycle, identifying the different types of models that should be used and re‐used at different life cycle stages (including systems engineering models of the actual system, domain‐specific simulation models, models of data processing pipelines, etc.). We highlight some approaches in MDE that can help create and manage these models and present a roadmap for research towards MDE of digital twins. [ABSTRACT FROM AUTHOR]
Copyright of Systems Engineering is the property of Wiley-Blackwell 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: Model‐Driven Engineering for Digital Twins: Opportunities and Challenges.
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  Data: <searchLink fieldCode="JN" term="%22Systems+Engineering%22">Systems Engineering</searchLink>. Sep2025, Vol. 28 Issue 5, p659-670. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Model-driven+software+architecture%22">Model-driven software architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+engineering%22">Systems engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Data+pipelining%22">Data pipelining</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink>
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  Data: Digital twins are increasingly used across a wide range of industries. Modeling is a key to digital twin development—both when considering the models which a digital twin maintains of its real‐world complement ("models in digital twin") and when considering models of the digital twin as a complex (software) system itself. Thus, systematic development and maintenance of these models is a key factor in effective and efficient digital twin development, maintenance, and use. We argue that model‐driven engineering (MDE), a field with almost three decades of research, will be essential for improving the efficiency and reliability of future digital twin development. To do so, we present an overview of the digital twin life cycle, identifying the different types of models that should be used and re‐used at different life cycle stages (including systems engineering models of the actual system, domain‐specific simulation models, models of data processing pipelines, etc.). We highlight some approaches in MDE that can help create and manage these models and present a roadmap for research towards MDE of digital twins. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Systems Engineering is the property of Wiley-Blackwell 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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        Value: 10.1002/sys.21815
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        Text: English
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