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Foundational Research Gaps and Future Directions for Digital Twins (2024) / Chapter Skim
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4 The Physical Counterpart: Foundational Research Needs and Opportunities
Pages 69-77

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From page 69...
... The Challenges Surrounding Data Acquisition for Digital Twins  Undersampling in complex systems with large spatiotemporal variability is a significant challenge for acquiring the data needed to characterize and quantify the dynamic physical and biological systems for digital twin development.  The complex systems that may make up the physical counterpart of a digital twin often exhibit intricate patterns, nonlinear behaviors, feedback, and emergent phenomena that require comprehensive sampling in order to develop an under 69
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From page 70...
... Finally, data acquisition efforts are often enhanced by a collaborative and multidisciplinary approach, combining expertise in data acquisition, modeling, and system analysis, to address the task holistically and with an understanding of how the data will move through the digital twin. Data Accuracy and Reliability  Digital twin technology relies on the accuracy and reliability of data, which requires tools and methods to ensure data quality, efficient data storage, management, and accessibility.
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From page 71...
... Increasing calls for transparency in the utilization of personal data have been made,d as well as for personal control of data, with several examples related to electronic health records.e These regulations that call for de-identification of the data do not address the elevated risks to privacy in the context of digital twins, and updates to regulations and data protection practices will need to address specific risks associated with digital twins. Modelers need to keep this in mind when designing systems that will typi cally require periodic collection of data, as study protocols submitted to human subjects' protections programs (Institutional Review Boards)
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From page 72...
... Developing digital twins that do not ignore salient rare events requires rethinking loss functions and performance metrics used in data-driven contexts. A fundamental challenge in decision-making may arise from discrepancies between the data streamed from the physical model and that which is predicted by the digital twin.
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From page 73...
... A related set of research questions around optimal sensor placement, sensor steering, and sensor dynamic scheduling is discussed in Chapter 6.  DATA INTEGRATION FOR DIGITAL TWINS  Increased access to diverse and dynamic streams of data from sensors and instruments can inform decision-making and improve model reliability and robustness. The digital twin of a complex physical system often gets data in different formats from multiple sources with different levels of verification and validation (e.g., visual inspection, record of repairs and overhauls, and quantitative sensor data from a limited number of locations)
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From page 74...
... For instance, ML methods used within digital twins need to be optimized to facilitate data assimilation with large-scale streaming data, and data assimilation methods that leverage ML models, architectures, and computational frameworks need to be developed. The scalability of data storage, movement, and management solutions becomes an issue as the amount of data collected from digital twin systems increases.  In some settings, the digital twin will face computational resource constraints (e.g., as a result of power constraints)
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From page 75...
... Finally, note that the data quality challenges outlined above are present in the large-scale streaming data setting as well, making the challenge of adaptive model training in the presence of anomalies and outliers that may correspond to either sensor failures or salient rare events particularly challenging. Data Fusion and Synchronization  Digital twins can integrate data from different data streams, which provides a means to address missing data or data sparsity, but there are specific concerns regarding data synchronization (e.g., across scales)
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From page 76...
... Fundamental chal lenges include aggregating uncertainty across different data modalities and scales as well as addressing missing data. Strategies for data sharing and collaboration must address challenges such as data ownership and intellec tual property issues while maintaining data security and privacy.  Challenges with Data Access and Collaboration Digital twins are an inherently multidisciplinary and collaborative effort.
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From page 77...
... Undersampling in complex systems with large spatiotemporal variability is a 2 significant challenge for acquiring the data needed for digital twin development. This undersampling could result in an incomplete characterization of the system and lead to overlooking critical events or significant features.
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Key Terms

  • digital twin
  • physical counterpart
  • data quality
  • data assimilation
  • data acquisition

  • rare events
  • twin technology
  • electronic health
  • virtual representation
  • integrating data

  • missing data
  • salient rare
  • assimilation methods
  • key gaps
  • training data

  • integrate data
  • maintaining data
  • data storage
  • streaming data
  • data flow

  • data security
  • documenting data
  • data integration
  • limited data
  • physical sensor

  • physical model
  • significant challenge
  • unique challenges
  • multiple sources
  • health records


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