Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 6 of 6 for “"Data-driven Reduced Order Modeling"”.
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Flow control and sensing using data-driven reduced-order modeling
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms
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Physics-based machine learning and data-driven reduced-order modeling
… reliable predictions for conditions outside the data used to train them. These models must also be able to make predictions that enforce physical constraints. Achieving these tasks is particularly challenging for the case of systems governed by partial differential equations, where generating …
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Multivariate Rational Approximation in Action: From Data-driven Modeling to Nonlinear Eigenvalue Problems
In this dissertation, we develop data-driven reduced-order modeling and model order reduction techniques that rely on multivariate approximation methods as a fundamental tool. The investigated techniques aim to capture the complex behavior of physical systems based on given experimental …
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Dimension Reduction in Structured Dynamical Systems: Optimal-𝓗<sub>2</sub> Approximation, Data-Driven Balancing, and Real-Time Monitoring
… a variety of problems pertaining to the model-order reduction, data-driven reduced-order modeling, and real-time monitoring of large-scale and structured dynamical systems. In the first part, balancing-based methods for system-theoretic model reduction of linear time-invariant systems are …
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Aerodynamic Enhancement and Reduced Order Modeling of Vertical Axis Wind Turbines
… under stable flow conditions. Finally, a reduced order model (ROM) is investigated for VAWT. A novel and robust CFDROM framework is built to evaluate the complex flow field behaviour of a single rotor 3-blade VAWT. A data-driven framework was developed following high-fidelity transient …
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Analytical and computational methods for non-Gaussian reliability analysis of nonlinear systems operating in stochastic environments
… typically must be repeated many times in order to characterize statistical results engineers are interested in. Unfortunately, this “iron law of Monte Carlo techniques" quickly leads to economically infeasible requirements on the number of experiment or simulation repeats, especially …