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Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy
Astrophysics and cosmology are rich with data. The advent of wide-area d...
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Digital Twin: Enabling Technology, Challenges and Open Research
Digital Twin technology is an emerging concept that has recently become ...
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Preliminary Exploration on Digital Twin for Power Systems: Challenges, Framework, and Applications
Digital twin (DT) is one of the most promising enabling technologies for...
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Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care
The health needs of those living in resource-limited settings are a vast...
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Synthetic Knowing: The Politics of the Internet of Things
All knowing is material. The challenge for Information Systems (IS) rese...
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Status Quo, Critical Reflection and Road Ahead of Digital Nudging in Information Systems Research – A Discussion with Markus Weinmann and Alexey Voinov
Research on Digital Nudging has become increasingly popular in the Infor...
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The role of surrogate models in the development of digital twins of dynamic systems
Digital twin technology has significant promise, relevance and potential...
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Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions
Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation and forecasting. However, the theoretical framework and practical implementations of digital twins (DT) are still far from this vision. Although successful implementations exist, sufficient implementation details are not publicly available, therefore it is difficult to assess their effectiveness, draw comparisons and jointly advance the DT methodology. This work explores the various DT features and current approaches, the shortcomings and reasons behind the delay in the implementation and adoption of digital twin. Advancements in machine learning, internet of things and big data have contributed hugely to the improvements in DT with regards to its real-time monitoring and forecasting properties. Despite this progress and individual company-based efforts, certain research gaps exist in the field, which have caused delay in the widespread adoption of this concept. We reviewed relevant works and identified that the major reasons for this delay are the lack of a universal reference framework, domain dependence, security concerns of shared data, reliance of digital twin on other technologies, and lack of quantitative metrics. We define the necessary components of a digital twin required for a universal reference framework, which also validate its uniqueness as a concept compared to similar concepts like simulation, autonomous systems, etc. This work further assesses the digital twin applications in different domains and the current state of machine learning and big data in it. It thus answers and identifies novel research questions, both of which will help to better understand and advance the theory and practice of digital twins.
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