Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations
Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations
Dokument öffnen (2.3MB)Datum der Veröffentlichung
10.2018Verlag / herausgebende Einrichtung
Institut für Numerische Simulation (INS)Verlagsort
BonnMetadaten
Zur Langanzeige
Zitierbarer Link (Handle URI) 
https://hdl.handle.net/20.500.11811/11826
Inhalt
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.
Schlagwörter
stochastic collocation, incompressible two-phase Navier–Stokes, uncertainty quantification
Klassifikation (DDC)
510 Mathematik 518 Numerische AnalysisErstpublikation
https://ins.uni-bonn.de/publication/preprintsZugehörige Publikation(en)
https://doi.org/10.1615/Int.J.UncertaintyQuantification.2019029228Reihe, Nummer
INS Preprints ; 1813Michael Griebel; Christian Rieger; Peter Zaspel: Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations. In: INS Preprints, 1813.
Online-Ausgabe in bonndoc: https://hdl.handle.net/20.500.11811/11826
Online-Ausgabe in bonndoc: https://hdl.handle.net/20.500.11811/11826
@unpublished{handle:20.500.11811/11826,
author = {{ } and { } and { }},
title = {Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations},
publisher = {Institut für Numerische Simulation (INS)},
year = 2018,
month = oct,
INS Preprints},
volume = 1813,
note = {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.},
url = {https://hdl.handle.net/20.500.11811/11826}
}
author = {{ } and { } and { }},
title = {Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations},
publisher = {Institut für Numerische Simulation (INS)},
year = 2018,
month = oct,
INS Preprints},
volume = 1813,
note = {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.},
url = {https://hdl.handle.net/20.500.11811/11826}
}
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