IEEE Access· 2020Q1· Review
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective
- 1,743citations
- Q1SCImago
- 2020year
Short summary
This paper reviews digital twin methodologies and techniques from a modeling perspective, highlighting their transformative role in designing and operating cyber-physical systems.
AI-generated from the title and abstract; the full text is not read.
Key points
- Digital twins are virtual representations of physical assets, enabled by data and simulators.
- Advances in AI, big data, and computational tools are making digital twins more feasible.
- The paper reviews digital twin construction from a modeling perspective.
- It identifies challenges, enabling technologies, and provides recommendations for stakeholders.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. IEEE Access, 2020 · DOI ↗
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Field: Industrial and Manufacturing Engineering
Industrial and Manufacturing EngineeringEngineering