Titlebook: Handbook of Dynamic Data Driven Applications Systems; Volume 2 Frederica Darema,Erik P. Blasch,Alex J. Aved Book 2023 This is a U.S. govern

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From Data to Decisions: A Real-Time Measurement–Inversion–Prediction–Steering Framework for Hazardouion of the task of sensor steering as an optimization problem that seeks to minimize prediction uncertainty, and a model reduction approach that facilitates execution of all of these steps in a real-time setting.

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Dynamic Data-Driven Application Systems for Reservoir Simulation-Based Optimization: Lessons Learnedrpose of this chapter is to review the fundamental components that have shaped reservoir-simulation-based optimization in the context of DDDAS. The foundations of each component will be systematically reviewed, followed by a discussion on current and future trends oriented to highlight the outstandi

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A Simulation-Based Online Dynamic Data-Driven Framework for Large-Scale Wind-Turbine Farm Systems Ope progresses. Online optimization is a suitable framework for problems involving data uncertainties that evolve over time, requiring important and cognizant system decisions to be made sequentially prior to observing the entire data stream. An important challenge in using online control is to optimi

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Toward Dynamic Data-Driven Systems for Rapid Adaptive Interdisciplinary Ocean Forecastingodels, and ocean current monitoring data assimilation schemes with innovative modeling and adaptive sampling methods. The legacy systems are encapsulated at the binary level using software component methodologies. Measurement models are utilized to link the observed data to the dynamical model varia

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Berechnung der Kegelradgeometrie,or ballistic trajectory estimation, where both the dynamic model and the angle-only measurement model are nonlinear. Numerical results show that the proposed PC-SrEnKF filter outperforms some previous popular nonlinear estimation methods, such as the extended Kalman filter (EKF), the unscented Kalma