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Pages 57-61

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From page 57...
... Matching well-known, model-driven digital twin representations with uncharacterized data-driven models requires attention to how the various levels of fidelity comprised in these models interact with each other in ways that may result in unanticipated overall digital twin behavior and inaccurate representation at the macro level. Another gap lies in the challenge of choosing the specific data collection points to adequately represent the effects of the less-characterized elements and augment the model-driven elements without oversampling the behavior already represented in the model-driven representations.
From page 58...
... As noted in Conclusion 2-2, a gap exists between the class of problems that has been considered in VVUQ for traditional modeling and simulation settings and the VVUQ problems that will arise for digital twins. Hybrid models––in particular those that infuse some form of black-box deep learning––represent a particular gap in this regard.  Finding 3-5: Hybrid modeling approaches that combine data-driven and mechanistic modeling approaches are a productive path forward for meeting the modeling needs of digital twins, but their effectiveness and practical use are limited by key gaps in theory and methods. 
From page 59...
... Additional examples of the integration of components are shown in Box 3-1. Interoperability of software and data are a challenge across domains and pose a particular challenge when integrating component and subsystem digital twins.
From page 60...
... Modeling the interaction be tween blood flow and the heart tissue captures the effects of fluid-structure inter action. The digital twin can incorporate regulatory mechanisms that control heart rate, blood pressure, and other physiological variables that maintain homeostasis and response mechanisms.
From page 61...
... Each one of these challenges highlights gaps in the current state of the art in surrogate modeling, as the committee discusses in more detail in the following. Surrogate modeling is an enabler for computationally efficient digital twins, but there is a limited understanding of trade-offs associated with collections of surrogate models operating in tandem in digital twins, the effects of multiphysics coupling on surrogate model accuracy, performance in high-dimensional settings, surrogate model VVUQ -- especially in extrapolatory regimes––and, for datadriven surrogates, costs of generating training data and learning.  Surrogate Modeling for High-Dimensional, Complex Multidisciplinary Systems State-of-the-art surrogate modeling has made considerable progress for simpler systems but remains an open challenge at the level of complexity needed for digital twins.

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