Foundational Research Gaps and Future Directions for Digital Twins (2024) / Chapter Skim
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" to describe digital twins of physical systems in the broadest sense possible, including the engineered world, natural phenomena, biological entities, and social systems. This definition introduces the phrase "predictive capability" to emphasize that a digital twin must be able to issue predictions beyond the available data to drive decisions that realize value.
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While there is significant enthusiasm around industry developments and applications of digital twins, the focus of this report is on identifying research gaps and opportunities. The report's recommendations are particularly targeted toward what agencies and researchers can do to advance mathematical, statistical, and computational foundations of digital twins.
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Human–digital twin interactions may also involve the human playing a crucial role in designing, managing, and operating elements of the digital twin, including selecting sensors and data sources, managing the models underlying the virtual representation, and implementing algorithms and analytics tools. Finding 2-1: A digital twin is more than just simulation and modeling. Conclusion 2-1: The key elements that comprise a digital twin include (1)
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ADVANCING DIGITAL TWIN STATE OF THE ART REQUIRES AN INTEGRATED RESEARCH AGENDA Despite the existence of examples of digital twins providing practical impact and value, the sentiment expressed across multiple committee information-gathering sessions is that the publicity around digital twins and digital twin solutions currently outweighs the evidence base of success.
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This integrated research agenda includes foundational needs that span multiple domains as well as domain-specific needs. Recommendation 1: Federal agencies should launch new crosscutting programs, such as those listed below, to advance mathematical, statisti cal, and computational foundations for digital twins. As these new digital twin–focused efforts are created and launched, federal agencies should identify opportunities for cross-agency interactions and facilitate cross community collaborations where fruitful. An interagency working group may be helpful to ensure coordination. • National Science Foundation (NSF)
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DoD should also consider using mechanisms such as the Multidisciplinary University Research Initia tive and Defense Acquisition University to support research efforts to develop and mature the tools and techniques for the application of digital twins as part of system digital engineering and model-based system engineering processes. • Other federal agencies. Many federal agencies and organizations be yond those listed above can play important roles in the advancement of digital twin research.
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is an area of particular need that necessitates collaborative and interdisciplinary investment to advance the responsible development, implementation, monitoring, and sustainability of digital twins. Evolution of the physical counterpart in real-world use conditions, changes in data collection, noisiness of data, addition and deletion of data sources, changes in the distribution of the data shared with the virtual twin, changes in the prediction and/or decision tasks posed to the digital twin, and evolution of the digital twin virtual models all have consequences for VVUQ.
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This limits the applicability of the model for some purposes, such as uncertainty quantification, probabilistic prediction, scenario testing, and visualization. Finding 3-2: Different applications of digital twins drive different require ments for modeling fidelity, data, precision, accuracy, visualization, and time-to-solution, yet many of the potential uses of digital twins are currently intractable to realize with existing computational resources.
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and surrogate model training. Physical Counterpart: Foundational Research Needs and Opportunities Digital twins rely on observation of the physical counterpart in conjunction with modeling to inform the virtual representation.
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On the virtual-to-physical flowpath, the digital twin is used to drive changes in the physical counterpart itself, or in the observational systems associated with the physical counterpart through an automatic controller or a human. Accordingly, the committee identified gaps associated with the use of digital twins for automated decision-making tasks, for providing decision support to a human decision-maker, and for decision tasks that are shared jointly within a human–agent team.
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Data assimi lation techniques are needed for data from multiple sources at different scales and numerical models with different levels of uncertainty. • Methods and tools are needed to make sensitivity information more read ily available for model-centric digital twins, including automatic differ entiation capabilities that will be successful for multiphysics, multiscale digital twin virtual representations, including those that couple multiple codes, each simulating different components of a complex system.
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TOWARD SCALABLE AND SUSTAINABLE DIGITAL TWINS Realizing the societal benefits of digital twins will require both incremental and more dramatic research advances in cross-disciplinary approaches. In addition to bridging fundamental research challenges in statistics, mathematics, and computing, bringing complex digital twins to fruition necessitates robust and reliable yet agile and adaptable integration of all these disparate pieces.
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For example, the Earth system science community is a leader in data assimilation; many fields of engineering are leaders in integrating VVUQ into simulation-based decision-making; and the medical community has a strong culture of prioritizing the role of a human decision-maker when advancing new technologies. Cross-domain interactions through the common lens of digital twins are opportunities to share, learn, and cross-fertilize. Conclusion 7-2: As the foundations of digital twins are established, it is the ideal time to examine the architecture, interfaces, bidirectional workflows of the virtual twin with the physical counterpart, and community prac tices in order to make evolutionary advances that benefit all disciplinary communities. Recommendation 5: Agencies should collaboratively and in a coordi nated fashion provide cross-disciplinary workshops and venues to foster identification of those aspects of digital twin research and development that would benefit from a common approach and which specific research
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• National Science Foundation -- Directorate for Technology, Innova tion and Partnerships programs. There is a history of both sharing and coordination of models within the international climate research community as well as a consistent commitment to data exchange that is beneficial to digital twins. While other disciplines have open-source or shared models, few support the breadth in scale and the robust integration of uncertainty quantification that are found in Earth system models and workflows. A greater level of coordination among the multidisciplinary teams of other complex systems, such as biomedical systems, would benefit maturation and cultivate the adoption of digital twins.
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These critical skill sets include but are not limited to systems engineering, systems thinking and architecting, data analytics, ML/AI, statistical/ probabilistic modeling and simulation, uncertainty quantification, computational mathematics, and decision science. These disciplines are rarely taught within the same academic curriculum.
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