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Pages 62-71

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From page 62...
... A further challenge is ensuring model fidelity and fitness for purpose when multiple physical processes interact.   Finding 3-7: State-of-the-art literature and practice show advances and suc cesses in surrogate modeling for models that form one discipline or one component of a complex system, but theory and methods for surrogates of coupled multiphysics systems are less mature.    An additional further challenge in dealing with surrogate models for digital twins of complex multidisciplinary systems is that the dimensionality of the parameter spaces underlying the surrogates can become high.
From page 63...
... A careful accounting of these elements is essential to understanding the cost–benefit trade-offs associated with surrogate models in digital twins. In tandem, advances in surrogate modeling methods for handling limited training data are needed.
From page 64...
... While data-driven surrogate modeling methods are attractive because they reduce the computational intractability of complex modeling and require limited effort to implement, important questions remain about how well they generalize or extrapolate in realms beyond the experience of their training data. This is particularly relevant in the context of digital twins, where ideally the digital twin would explore "what if" scenarios, potentially far from the domain of the available training data -- that is, where the digital twin must extrapolate to previously unseen settings.
From page 65...
... Warnings for extrapolations are particularly important in digital twins of critical systems. continued
From page 66...
... Coupled multiphysics systems pose particular challenges to surrogate modeling 2 approaches that are not addressed by state-of-the-art methodology. There is a gap between the complexity of problems for which mathematical theory and scalable algorithms exist for surrogate modeling and the class of problems that underlies high-impact applications of digital twins.
From page 67...
... 2023. "Building Robust Digital Twins." Presentation to the Committee on Foundational Research Gaps and Future Directions for Digital Twins.
From page 68...
... 2023. "Digital Twins in Oncology." Presentation to the Workshop on Opportunities and Challenges for Digital Twins in Biomedical Sciences.
From page 69...
... While significant literature has been devoted to the science and best practices around gathering and preparing data for use, this chapter focuses on the most important gaps and opportunities that are crucial for robust digital twins. DATA ACQUISITION FOR DIGITAL TWINS  Data collection for digital twins is a continual process that plays a critical role in the development, refinement, and validation of the models that comprise the virtual representation.
From page 70...
... A key research challenge for digital twins is the development of methods for data quality assessment that ensure digital twins are robust to spurious outliers while accurately representing salient rare events. Several technical challenges must be addressed here.
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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