Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations

Griebel, Michael and Rieger, Christian and Zaspel, Peter. (2019) Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations. International Journal for Uncertainty Quantification, 9 (5). pp. 471-492.

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Official URL: https://edoc.unibas.ch/70342/

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In this work, we apply stochastic collocation methods with radial kernel basis functions for an uncertainty quantification of the random incompressible two-phase Navier-Stokes equations. Our approach is nonintrusive and we use the existing fluid dynamics solver NaSt3DGPF to solve the incompressible two-phase Navier-Stokes equation for each given realization. We are able to empirically show that the resulting kernel-based stochastic collocation is highly competitive in this setting and even outperforms some other standard methods.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Mathematik > Computational Mathematics (Harbrecht)
UniBasel Contributors:Zaspel, Peter
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Begell House
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:13 Nov 2020 15:52
Deposited On:13 Nov 2020 15:52

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