Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, bi...

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Vydáno v:IEEE access Ročník 8; s. 21980 - 22012
Hlavní autoři: Rasheed, Adil, San, Omer, Kvamsdal, Trond
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial intelligence
Avatars
Big Data
big data cybernetics
Biological system modeling
Buildings
Cybernetics
Data processing
Decision making
Digital twin
Digital twins
hybrid analysis and modeling
machine learning
Modularity
Monitoring
Optimization
Real-time systems
Simulators
Solid modeling
Solvers
Twinning
Virtual prototyping
ISSN:2169-3536
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Shrnutí:Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.
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ISSN:2169-3536
DOI:10.1109/ACCESS.2020.2970143