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

2018/10/26 by Michael Griebel, Griebel, Michael, Christian Rieger +3
Decision Sciences · Environmental Science · Physics and Astronomy · #FOS: Mathematics #Hydrology and Drought Analysis #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Probabilistic and Robust Engineering Design

paper · pdf · doi:10.48550/arxiv.1810.11270

openalex publication_date 2018/10/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this work, we apply stochastic collocation methods with radial kernel\nbasis functions for an uncertainty quantification of the random incompressible\ntwo-phase Navier-Stokes equations. Our approach is non-intrusive and we use the\nexisting fluid dynamics solver NaSt3DGPF to solve the incompressible two-phase\nNavier-Stokes equation for each given realization. We are able to empirically\nshow that the resulting kernel-based stochastic collocation is highly\ncompetitive in this setting and even outperforms some other standard methods.\n

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