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Pages 1-11

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From page 1...
... " 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.
From page 2...
... Digital twins can be a critical tool for decision-making based on a synergistic combination of models and data. The bidirectional interplay between a physical system and its virtual representation endows the digital twin with a dynamic nature that goes beyond what has been traditionally possible with modeling and simulation, creating a virtual representation that evolves with the system over time.
From page 3...
... This may lead to the digital twin including high-fidelity, simplified, or surrogate models, as well as a mixture thereof. Furthermore, a digital twin may include the ability to represent and query the virtual models at variable levels of resolution and fidelity depending on the particular task at hand and the available resources (e.g., time, computing, bandwidth, data)
From page 4...
... 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.
From page 5...
... 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)
From page 6...
... 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.
From page 7...
... 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.
From page 8...
... Federal agencies should consider the Department of Energy Predictive Science Academic Alliance Program as a possible model to emulate. Virtual Representation: Foundational Research Needs and Opportunities A fundamental challenge for digital twins is the vast range of spatial and temporal scales that the virtual representation may need to address.
From page 9...
... 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.
From page 10...
... 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.
From page 11...
... Scal able and efficient optimization and uncertainty quantification methods that handle non-differentiable functions that arise with many risk metrics are also lacking. • Scalable methods are needed for goal-oriented sensor steering and optimal experimental design that encompass the full sense–assimilate–predict– control–steer cycle while accounting for uncertainty.  • Development of implementation science around digital twins, user-cen tered design of digital twins, and effective human–digital twin teaming is needed.  • Research is needed on the impact of the content, context, and mode of human–digital twin interaction on the resulting decisions.


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