Foundational Research Gaps and Future Directions for Digital Twins (2024) / Chapter Skim
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2 The Digital Twin Landscape
2 The Digital Twin Landscape
Pages 21-48
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DEFINITIONS Noting that the scope of this study is on identifying foundational research gaps and opportunities for digital twins, it is important to have a shared understanding of the definition of a digital twin. For the purposes of this report, the committee uses the following definition of a digital twin: A digital twin is a set of virtual information constructs that mimics the structure, context, and behavior of a natural, engineered, or social system (or system of-systems)
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In place of the term "asset," the committee refers to "a natural, engineered, or social system (or system-of-systems) " to describe digital twins of physical systems in the broadest sense possible, including the engineered world, natural phenomena, biological entities, and social systems.
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Many of the notions underlying digital twins also have a long history in other fields, such as model predictive control, which similarly combines models and data in a bidirectional feedback loop (Rawlings et al.
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More details are provided in the following subsections. The Physical Counterpart and Its Virtual Representation There are numerous and diverse examples of physical counterparts for which digital twins are recognized as bringing high potential value, including aircraft, body organs, cancer tumors, cities, civil infrastructure, coastal areas, farms, forests, global atmosphere, hospital operations, ice sheets, nuclear reactors, patients, and many more.
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Decision tasks might include personalized therapy control decisions, such as the dose and schedule of delivery of therapeutics over time, and data collection decisions, such as the frequency of serial imaging studies, blood tests, and other clinical assessments. These deci sions can be automated as part of the digital twin or made by a human informed by the digital twin's output.a Digital Twin of an Aircraft Engine The virtual representation of an aircraft engine might comprise machine learn ing (ML)
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. The set of models comprising the virtual representation of a digital twin of a complex system will span multiple disciplines and multiple temporal and spatial scales.
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Decision tasks might include actions related to policy-making, energy system design, deployment of new observing systems, and emergency prepared ness for extreme weather events. These decisions may be made automatically as part of the digital twin or made by a human informed by the digital twin's output.c Digital Twin of a Manufacturing Process Manufacturing environments afford many opportunities for digital twins.
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If this need is addressed by, for example, the use of Bayesian formulations, then the formulation of the virtual representation must also define prior information for parameters, numerical model parameters, and states. Bidirectional Feedback Flow Between Physical and Virtual The bidirectional interaction between the virtual representation and the physical counterpart forms an integral part of the digital twin.
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User-centered design is central to extracting value from the digital twin. Verification, Validation, and Uncertainty Quantification VVUQ is essential for the responsible development, implementation, monitoring, and sustainability of digital twins.
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The challenges lie in the features that set digital twins apart from traditional modeling and simulation, with the most important difference being the bidirectional feedback loop between the virtual and the physical. Evolution of the physical counterpart in real-world use conditions, changes in data collection hardware and software, 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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. Particularly unique to digital twins is inclusion of uncertainties due to integration of multiple modalities of data and models, and bidirectional and sometimes realtime interaction between the virtual representation, the physical counterpart, and the possible human-in-the-loop interactions.
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Conclusion 2-2: Digital twins require VVUQ to be a continual process that must adapt to changes in the physical counterpart, digital twin virtual models, data, and the prediction/decision task at hand. A gap exists between the class of problems that has been considered in traditional modeling and simulation settings and the VVUQ problems that will arise for digital twins. The importance of a rigorous VVUQ process for a potentially powerful tool such as a digital twin cannot be overstated.
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One wonders: Is it the methods themselves that pose a risk to the human enterprise, or is it the way in which they are deployed without due attention to VVUQ and certification? When it comes to digital twins and their deployment in critical engineering and scientific applications, humanity cannot afford the cavalier attitude that pervades other applications of AI.
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In both the biomedical workshop and atmospheric and climate sciences workshop on digital twins, speakers warned of the bias inherent in algorithms due to missing data as a result of historical and systemic biases (NASEM 2023a,b)
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Below, the committee identifies some novel challenges that arise in the context of digital twins. By virtue of the personalized nature of a digital twin (i.e., the digital twin's specificity to a unique asset, human, or system) , the virtual construct aggregates sensitive data, potentially identifiable or re-identifiable, and models that offer tailored insights about the physical counterpart.
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A malicious actor can inject an attack into the feedback loop (e.g., spoofing as the digital twin) and influence the physical system in a harmful manner. An additional novel area of security consideration for digital twins arises from the vision of an ideal future where digital twins scale easily and effortlessly.
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This level of innovation is facilitated by a digital twin's ability to integrate a product's entire life cycle with performance data and to employ a continuous loop of optimization. Ultimately, digital twins could reduce risk, accelerate time from design to production, and improve decision-making as well as connect real-time data with virtual representations for remote monitoring, predictive capabilities, collaboration among stakeholders, and multiple training opportunities (Bochenek 2023)
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Managing massive amounts of data and applying advanced analytics with a new level of intelligent decision-making will be needed to fully take advantage of digital twins in the future. There is also a need for further research in ontologies and harmonization among the digital twin user community; interoperability (from cells, to units, to systems, to systems-of-systems)
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It is important to note that climate predictions do not necessarily require realtime updates, but some climate-related issues, such as wildfire response planning, might (Ghattas 2023) . Three specific thrusts could help to advance the sort of climate modeling needed to realize digital twins: research on parametric sparsity and generalizing observational data, generation of training data and computation for highest possible resolution, and uncertainty quantification and calibration based on both observational and synthetic data (Schneider 2023)
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. Digital Twin Examples, Needs, and Opportunities for Biomedical Applications Many researchers hold that digital twins are not yet in practical use for decision-making in the biomedical space, but extensive work to advance their development is ongoing.
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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-gath 6 The website for the Swedish Digital Twin Consortium is https://www.sdtc.se, accessed June 30, 2023.
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It is important to separate the aspirational from the actual to strengthen the credibility of the research in digital twins and to recognize that serious research questions remain in order to achieve the aspirational. Conclusion 2-6: Realizing the potential of digital twins requires an inte grated research agenda that advances each one of the key digital twin ele ments and, importantly, a holistic perspective of their interdependencies and interactions. 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.
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. DoD's Office of the Under Secretary of Defense for Research and Engineering should advance the appli cation of digital twins as an integral part of the digital engineering performed to support system design, performance analysis, devel opmental and operational testing, operator and force training, and operational maintenance prediction.
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For example, the National Oceanic and Atmospheric Administration, the National Institute of Stan dards and Technology, and the National Aeronautics and Space Administration should be included in the discussion of digital twin research and development, drawing on their unique missions and extensive capabilities in the areas of data assimilation and real time decision support. As described earlier in this chapter, VVUQ is a key element of digital twins that necessitates collaborative and interdisciplinary investment.
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Methods for validating atmospheric, climate, and sustainability sciences digital twin 1 predictions over long horizons and extreme events are needed. Mechanisms to better facilitate cross-disciplinary collaborations are needed to 2 achieve inclusive digital twins for atmospheric, climate, and sustainability sciences. Due to the heterogeneity, complexity, multimodality, and breadth of biomedical 1 data, the harmonization, aggregation, and assimilation of data and models to effectively combine these data into biomedical digital twins require significant technical research. Research Base Exists with Opportunities to Advance Digital Twins Uncertainty quantification is critical to digital twins for atmospheric, climate, and 2 sustainability sciences and will generally require surrogate models and/or improved sampling techniques.
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2022. "Digital Twins: Essential, Mathematical, Statistical and Computing Research Foundations." Presentation to the Committee on Foundational Research Gaps and Future Direc tions for Digital Twins.
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2023. "Prognostic Digital Twins in Practice." Presentation to the Workshop on Opportuni ties and Challenges for Digital Twins in Biomedical Sciences.
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2023. "Discussion of Ethical Considerations of Digital Twins." Presentation to the Committee on Foundational Research Gaps and Future Directions for Digital Twins.
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