The following pages link to Ilja Kröker (Q412314):
Displaying 15 items.
- Finite volume schemes for hyperbolic balance laws with multiplicative noise (Q412315) (← links)
- A stochastically and spatially adaptive parallel scheme for uncertain and nonlinear two-phase flow problems (Q723106) (← links)
- Finite volume methods for hyperbolic partial differential equations with spatial noise (Q1710801) (← links)
- Computational uncertainty quantification for some strongly degenerate parabolic convection-diffusion equations (Q1757392) (← links)
- Intrusive uncertainty quantification for hyperbolic-elliptic systems governing two-phase flow in heterogeneous porous media (Q2398880) (← links)
- Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario (Q2418682) (← links)
- Uncertainty Quantification for a Clarifier–Thickener Model with Random Feed (Q2902087) (← links)
- Stochastic Modeling for Heterogeneous Two-Phase Flow (Q2926188) (← links)
- A hybrid stochastic Galerkin method for uncertainty quantification applied to a conservation law modelling a clarifier-thickener unit (Q2933497) (← links)
- (Q4593438) (← links)
- Hybrid Stochastic Galerkin Finite Volumes for the Diffusively Corrected Lighthill-Whitham-Richards Traffic Model (Q5271119) (← links)
- Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the carbon dioxide benchmark (Q6074252) (← links)
- A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms (Q6162876) (← links)
- Global sensitivity analysis using multi-resolution polynomial chaos expansion for coupled Stokes-Darcy flow problems (Q6180112) (← links)
- The deep arbitrary polynomial chaos neural network or how deep artificial neural networks could benefit from data-driven homogeneous chaos theory (Q6488834) (← links)
