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

By Adil Rasheed, Omer San, Trond Kvamsdal. IEEE Access, 2019

1,454 citations recorded by the source corpus.

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.

Subfields: Industrial and Manufacturing Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/179ea04fc6feff8ae3ffdf471d575d5ddadb2e37

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In Industrial and Manufacturing Engineering

In Management Science and Operations Research

Citations in the corpus

The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities (2021, background): “And, the modeling perspective of digital twinning is explored by Rasheed et al. [10].”
Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems (2020, methodology): “In addition to Yang et al. [68] , Rasheed et al. [63] provided a perspective on some of the challenges for digi-B Fig.”
Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects (2022, background): “Rasheed et al. [12] created a comprehensive DT survey that analyses the paradigm from the perspectives of its value (expressed via services and software platforms), applications, enabling technologies, and challenges.”
A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, background): “, product design [12,13], modeling and simulation [14], fault diagnostics and prognostics [15].”
All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda (2021, background): “In order to protect digital twins, the metaverse has to ensure that the digital twins created and implemented are original [691].”
A probabilistic graphical model foundation for enabling predictive digital twins at scale (2020, background): “s associated physical asset, providing a principled mathematical foundation for creating, leveraging, and studying digital twins. Digital twins have garnered attention in a wide range of applications [1]. Structural digital twins have shown promise in virtual health monitoring, certication, and pred”