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Pages 50-56

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From page 50...
... There are other areas in which the state of the art in modeling provides potential enablers for digital twins. The fields of statistics, ML, and surrogate modeling have advanced considerably in recent years, but a gap remains between the class of problems that has been addressed and the modeling needs for digital twins.  Some communities focus on high-fidelity models in the development of digital twins while others define digital twins using simplified and/or surrogate models.
From page 51...
... . These kinds of analyses are an important ingredient of assessing fitness for purpose; however, the needs for digital twins go far beyond this, particularly given the range of model types that digital twins will employ and the likelihood that a digital twin will couple multiple models of differing fidelity.
From page 52...
... As discussed in the next section, an important need is to advance hybrid modeling approaches that leverage the synergistic strengths of data-driven and model-driven digital twin formulations. MULTISCALE MODELING NEEDS AND OPPORTUNITIES FOR DIGITAL TWINS A fundamental challenge for digital twins is the vast range of spatial and temporal scales that the virtual representation may need to address.
From page 53...
... Even so, a gap remains between the scales that can be simulated and actionable scales. An additional challenge is that as finer scales are resolved and a given model achieves greater fidelity to the physical counterpart it simulates, the computational and data storage/analysis requirements increase.
From page 54...
... Finding 3-4: Advancing mathematical theory and algorithms in both data driven and multiscale physics-based modeling to reduce computational needs for digital twins is an important complement to increased computing resources. Hybrid Modeling Combining Mechanistic Models and Machine Learning Hybrid modeling approaches -- synergistic combinations of empirical and mechanistic modeling approaches that leverage the best of both data-driven and model-driven formulations -- were repeatedly emphasized during this study's information gathering (NASEM 2023a,b,c)
From page 55...
... In climate and engineering applications, the potential for hybrid modeling to underpin digital twins is significant. In addition to modeling across scales as described above, hybrid models can help provide understandability and explainability.
From page 56...
... Buganza Tepole, W.R. Cannon, et al., 2019, "Integrating Machine Learning and Multiscale Modeling -- Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences," npj Digital Medicine 2(115)


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