Multi-level Monte Carlo finite volume methods for shallow water equations with uncertain topography in multi-dimensions
Open access
Datum
2011-11Typ
- Report
ETH Bibliographie
yes
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Abstract
The initial data and bottom topography, used as inputs in shallow water models, are prone to uncertainty due to measurement errors. We model this uncertainty statistically in terms of random shallow water equations. We extend the Multi-Level Monte Carlo (MLMC) algorithm to numerically approximate the random shallow water equations efficiently. The MLMC algorithm is suitably modified to deal with uncertain (and possibly uncorrelated) data on each node of the underlying topography grid by the use of a hierarchical topography representation. Numerical experiments in one and two space dimensions are presented to demonstrate the efficiency of the MLMC algorithm. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-a-010400202Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
SAM Research ReportBand
Verlag
Seminar for Applied Mathematics, ETH ZurichThema
Shallow water equations; Energy stable schemes; Uncertainty quantification; Multi- Level Monte Carlo; ParallelizationOrganisationseinheit
03435 - Schwab, Christoph / Schwab, Christoph
03851 - Mishra, Siddhartha / Mishra, Siddhartha
Förderung
247277 - Automated Urban Parking and Driving (EC)
Zugehörige Publikationen und Daten
Is previous version of: http://hdl.handle.net/20.500.11850/44358
ETH Bibliographie
yes
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