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Data-Driven Tsunami Propagation Modelling Using Proper Orthogonal Decomposition

Author(s): Dao My Ha

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Keywords: Tsunami; Data-driven; Reduced-order; Proper Orthogonal Decomposition

Abstract: Model reduction is a powerful tool allowing systematic generation of cost-efficient representations of large-scale computational systems, such as those resulting from discretization of partial differential equations (PDEs). In many cases, models for such applications yield very large systems that are computationally intensive to solve. A necessary element to permit decision-making in real-time is the development of accurate, efficient computational models that can be solved within required short time. This paper proposes a new data-driven/reduced-order approach for quick and accurate prediction of tsunami propagation. The Proper Orthogonal Decomposition (POD) technique is used for the reduced-order model, with the basis functions determined from an ensemble of offline high-fidelity simulations. Results show that the new methodology is able to predict the maximum tsunami height and travel time within a few seconds at a very high accuracy. The presented method can be used in operational tsunami warning systems.


Year: 2007

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