Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations
Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations
dc.contributor.author | Michael Griebel | |
dc.contributor.author | Christian Rieger | |
dc.contributor.author | Peter Zaspel | |
dc.date.accessioned | 2024-08-13T12:19:25Z | |
dc.date.available | 2024-08-13T12:19:25Z | |
dc.date.issued | 10.2018 | |
dc.identifier.uri | https://hdl.handle.net/20.500.11811/11826 | |
dc.description.abstract | 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. | en |
dc.format.extent | 24 | |
dc.language.iso | eng | |
dc.relation.ispartofseries | INS Preprints ; 1813 | |
dc.rights | In Copyright | |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
dc.subject | stochastic collocation | |
dc.subject | incompressible two-phase Navier–Stokes | |
dc.subject | uncertainty quantification | |
dc.subject.ddc | 510 Mathematik | |
dc.subject.ddc | 518 Numerische Analysis | |
dc.title | Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations | |
dc.type | Preprint | |
dc.publisher.name | Institut für Numerische Simulation (INS) | |
dc.publisher.location | Bonn | |
dc.rights.accessRights | openAccess | |
dc.relation.doi | https://doi.org/10.1615/Int.J.UncertaintyQuantification.2019029228 | |
ulbbn.pubtype | Zweitveröffentlichung | |
dcterms.bibliographicCitation.url | https://ins.uni-bonn.de/publication/preprints |
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