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Pages 83-90

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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.
From page 84...
... 2023. "Risk-Adaptive Decision-Making and Learning." Presentation to the Committee on Foundational Research Gaps and Future Directions for Digital Twins.
From page 85...
... This chapter also discusses the roles of digital twins for providing decision support to a human decision-maker and for decision tasks that are shared jointly within a human–agent team. The chapter concludes with a discussion of the ethical and social implications of the use of digital twins in decision-making.  PREDICTION, CONTROL, STEERING, AND DECISION UNDER UNCERTAINTY  Just as there is a broad range of model types and data that may compose a digital twin depending on the particular domain and use case, there is an equally broad range of prediction and decision tasks that a digital twin may be called on to execute and/or support.
From page 86...
... A digital twin of a subsurface region can guide decision-making on both sensing decisions and contaminant control decisions: where to drill observation wells, where to drill control wells, and what are optimal pumping/injection profiles at control wells.  Asset Performance Management Equipment maintenance and replacement efforts are improved using predic tion to mitigate breakdowns. Digital twins of various assets (e.g., pumps, com pressors, and turbines)
From page 87...
... Research gaps to address these challenges span many technical areas including operations research, reinforcement learning, optimal and stochastic control, dynamical systems, partial differential equation (PDE) constrained optimization, scalable algorithms, and statistics.  Rare Events and Risk Assessment in Support of Decision-Making  In many applications, digital twins will be called on to execute or support decisions that involve the characterization of low-probability events (e.g., failure in an engineering system, adverse outcomes in a medical intervention)
From page 88...
... On the other hand, Monte Carlo sampling becomes extremely inefficient, especially when dealing with low-probability events.  Finding 6-1: There is a need for digital twins to support complex trade-offs of risk, performance, cost, and computation time in decision-making.  Sensor Steering, Optimal Experimental Design, and Active Learning  Within the realm of decision-making supported and executed by digital twins is the important class of problems that impact the data -- specifically, the sensing and observing systems -- of the physical counterpart. These problems may take the form of sensor placement, sensor steering, and sensor dynamic scheduling, which can be broadly characterized mathematically as optimal experimental design (OED)
From page 89...
... . The resulting integrated sense–assimilate–predict–control–steer cycle is challenging and cuts across several traditional areas of study but ultimately will lead to the most powerful instantiation of digital twins; therefore, scalable methods for goal-oriented sensor steering and OED over the entire sense–assimilate–predict–control–steer cycle merit further exploration.
From page 90...
... In addition, since the coupled data assimilation and optimal control problems are solved repeatedly over a moving time window, there is an opportunity to exploit dynamically adaptive optimization and control algorithms that can exploit sensitivity information to warm-start new solutions. Scalable methods to achieve dynamic adaptation in digital twin decision-making are necessary for exploiting the potential of digital twins.


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