Foundational Research Gaps and Future Directions for Digital Twins (2024) / Chapter Skim
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5 Feedback Flow from Physical to Virtual: Foundational Research Needs and Opportunities
5 Feedback Flow from Physical to Virtual: Foundational Research Needs and Opportunities
Pages 78-84
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From page 78... ...
These challenges represent foundational gaps in inverse problem and data assimilation theory, methodology, and computational approaches. INVERSE PROBLEMS AND DIGITAL TWIN CALIBRATION Digital twin calibration is the process of estimating numerical model parameters for individualized digital twin virtual representations.
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From page 79... ...
Data-driven regularization approaches that incorporate more realistic priors are necessary for digital twins. Optimization of Numerical Model Parameters Under Uncertainty Another key challenge is to perform optimization of numerical model parameters (and any additional hyperparameters)
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From page 80... ...
that are not captured using only the mean and standard deviation are needed. DATA ASSIMILATION AND DIGITAL TWIN UPDATING Data assimilation tools have been used heavily in numerical weather forecasting, and they can be critical for digital twins broadly, including to improve model states based on current observations. Still, there is more to be exploited in the bidirectional feedback flow between physical and virtual beyond standard data assimilation (Blair 2021)
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From page 81... ...
Traceability of model hierarchies and reproducibility of results are not fully considered in existing data assimila tion approaches. Digital Twin Demands for Actionable Time Scales Most literature focuses on offline data assimilation, but the assimilation of real-time sensor data for digital twins to be used on actionable time scales will require advancements in data assimilation methods and tight coupling with the control or decision-support task at hand (see Chapter 6) . For example, the vast, global observing system of the Earth's atmosphere and numerical models of its dynamics and processes are combined in a data assimilation framework to create initial conditions for weather forecasts.
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From page 82... ...
Given data, Bayesian parameter estimation can be used to select the best model and to infer posterior probability distributions for numerical model parameters. Forward propagation of these distributions then leads to a posterior prediction, in which the digital twin aids decision-making by providing an estimate of the prediction quantities of interest and their uncertainties.
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From page 83... ...
TABLE 5-1 Key Gaps, Needs, and Opportunities for Enabling the Feedback Flow from the Physical Counterpart to the Virtual Representation of a Digital Twin Maturity Priority Early and Preliminary Stages Tools for tracking model and related data provenance (i.e., maintaining a history 1 of model updates and tracking model hierarchies) to handle scenarios where predictions do not agree with observed data are limited.
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From page 84... ...
Research Base Exists with Opportunities to Advance Digital Twins New approaches that incorporate more realistic prior distributions or data-driven 2 regularization are needed. Since uncertainty quantification is often necessary, fast Bayesian methods will need to be developed to make solutions operationally practical.
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