Foundational Research Gaps and Future Directions for Digital Twins (2024) / Chapter Skim
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3 Virtual Representation: Foundational Research Needs and Opportunities
3 Virtual Representation: Foundational Research Needs and Opportunities
Pages 49-68
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From page 49... ...
Surrogate modeling needs and opportunities for digital twins are also discussed, including surrogate modeling for high-dimensional, complex multidisciplinary systems and the essential data assimilation, dynamic updating, and adaptation of surrogate models. FIT-FOR-PURPOSE VIRTUAL REPRESENTATIONS FOR DIGITAL TWINS As discussed in Chapter 2, the computational models underlying the digital twin virtual representation can take many mathematical forms (including dynamical systems, differential equations, and statistical models)
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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.
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From page 51... ...
A key feature for determining fitness for purpose is assessing whether the fusion of a mathematical model, potentially corrected via a discrepancy function, and observational data provides relevant information for decision-making. Another key aspect of determining digital twin fitness for purpose is assessment of the integrity of the physical system's observational data, as discussed in Chapter 4. Finding 3-1: Approaches to assess modeling fidelity are mathematically mature for some classes of models, such as partial differential equations that represent one discipline or one component of a complex system; however, theory and methods are less mature for assessing the fidelity of other classes of models (particularly empirical models)
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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.
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From page 53... ...
This limits the applicability of the model for some purposes, such as uncertainty quantification, probabilistic prediction, scenario testing, and visualization. As a result, the demarcation between resolved and unresolved scales is often determined by computational constraints
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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)
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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.
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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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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.
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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.
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From page 59... ...
Another example is the coupling of human system models with Earth system models, which often differ in model fidelity as well as in mathematical forms. Furthermore, in the context of digital twins, some technical challenges remain in coupled model data assimilation, such as properly initializing each component model.
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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.
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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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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.
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From page 63... ...
Another challenge associated with surrogate models in digital twins is accounting for the data and computational resources needed to develop data-driven surrogates. While the surrogate modeling community has developed several compelling approaches in recent years, analyses of the speedups associated with these approaches in many cases do not account for the time and expense associated with generating training data or using complex numerical solvers at each iteration of the training process.
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From page 64... ...
At the same time, the surrogate models themselves must be updated -- and correspondingly validated -- as the digital twin virtual representation evolves. One set of research gaps is around the role of a surrogate model in accelerating digital twin state estimation (data assimilation) and parameter estimation (inverse problem)
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From page 65... ...
Entailing multiphysics coupling and high-dimensional parameter spaces as discussed above, the digital twin setting provides a particular challenge to achieving adaptation under computational constraints. Furthermore, the adaptation of a surrogate model will require an associated continual VVUQ workflow -- which again must be conducted under computational constraints -- so that the adapted surrogate may be used with confidence in the virtual-to-physical digital twin decision-making tasks.
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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.
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From page 67... ...
2023. "Building Robust Digital Twins." Presentation to the Committee on Foundational Research Gaps and Future Directions for Digital Twins.
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From page 68... ...
2018. "RB-FEA Based Digital Twin for Structural Integrity Assessment of Offshore Structures." Offshore Technology Conference.
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