TY - JOUR
T1 - Model‐Driven Engineering for Digital Twins: Opportunities and Challenges
AU - Michael, Judith
AU - Cleophas, Loek
AU - Zschaler, Steffen
AU - Clark, Tony
AU - Combemale, Benoit
AU - Godfrey, Thomas
AU - Khelladi, Djamel Eddine
AU - Kulkarni, Vinay
AU - Lehner, Daniel
AU - Rumpe, Bernhard
AU - Wimmer, Manuel
AU - Wortmann, Andreas
AU - Ali, Shaukat
AU - Barn, Balbir
AU - Barosan, Ion
AU - Bencomo, Nelly
AU - Bordeleau, Francis
AU - Grossmann, Georg
AU - Karsai, Gabor
AU - Kopp, Oliver
AU - Mitschang, Bernhard
AU - Muñoz Ariza, Paula
AU - Pierantonio, Alfonso
AU - Polack, Fiona A. C.
AU - Riebisch, Matthias
AU - Schlingloff, Holger
AU - Stumptner, Markus
AU - Vallecillo, Antonio
AU - van den Brand, Mark
AU - Vangheluwe, Hans
N1 - Copyright © 2025 The Author(s). Systems Engineering published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
PY - 2025/4/2
Y1 - 2025/4/2
N2 - 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.
AB - 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.
KW - model‐driven engineering
KW - digital twin
KW - systems engineering
KW - cyber‐physical systems
UR - https://incose.onlinelibrary.wiley.com/doi/10.1002/sys.21815
UR - http://www.scopus.com/inward/record.url?scp=105002060154&partnerID=8YFLogxK
U2 - 10.1002/sys.21815
DO - 10.1002/sys.21815
M3 - Article
SN - 1098-1241
JO - Systems Engineering
JF - Systems Engineering
ER -