| Finite volume schemes for hyperbolic balance laws with multiplicative noise I Kröker, C Rohde Applied Numerical Mathematics 62 (4), 441-456, 2012 | 54 | 2012 |
| Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario M Köppel, F Franzelin, I Kröker, S Oladyshkin, G Santin, D Wittwar, ... Computational Geosciences 23, 339-354, 2019 | 33 | 2019 |
| A hybrid stochastic Galerkin method for uncertainty quantification applied to a conservation law modelling a clarifier‐thickener unit R Bürger, I Kröker, C Rohde ZAMM‐Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte …, 2014 | 31 | 2014 |
| A stochastically and spatially adaptive parallel scheme for uncertain and nonlinear two-phase flow problems I Kröker, W Nowak, C Rohde Computational Geosciences 19, 269-284, 2015 | 23 | 2015 |
| Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory S Oladyshkin, F Mohammadi, I Kroeker, W Nowak Entropy 22 (8), 890, 2020 | 21 | 2020 |
| Computational uncertainty quantification for a clarifier-thickener model with several random perturbations: A hybrid stochastic Galerkin approach A Barth, R Bürger, I Kroeker, C Rohde Computers & Chemical Engineering 89, 11-26, 2016 | 18 | 2016 |
| Arbitrary multi-resolution multi-wavelet-based polynomial chaos expansion for data-driven uncertainty quantification I Kröker, S Oladyshkin Reliability Engineering & System Safety 222, 108376, 2022 | 12 | 2022 |
| Intrusive uncertainty quantification for hyperbolic-elliptic systems governing two-phase flow in heterogeneous porous media M Köppel, I Kröker, C Rohde Computational Geosciences 21, 807-832, 2017 | 10 | 2017 |
| Uncertainty Quantification for a Clarifier–Thickener Model with Random Feed R Bürger, I Kröker, C Rohde Finite Volumes for Complex Applications VI Problems & Perspectives: FVCA 6 …, 2011 | 10 | 2011 |
| Stochastic modeling for heterogeneous two-phase flow M Köppel, I Kröker, C Rohde Finite Volumes for Complex Applications VII-Methods and Theoretical Aspects …, 2014 | 9 | 2014 |
| Computational uncertainty quantification for some strongly degenerate parabolic convection–diffusion equations R Bürger, I Kröker Journal of Computational and Applied Mathematics 348, 490-508, 2019 | 7 | 2019 |
| A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms PC Bürkner, I Kröker, S Oladyshkin, W Nowak Journal of Computational Physics 488, 112210, 2023 | 6 | 2023 |
| Finite volume methods for conservation laws with noise I Kröker Finite volumes for complex applications V, 527-534, 2008 | 6 | 2008 |
| Datasets and executables of data-driven uncertainty quantification benchmark in carbon dioxide storage M Köppel, F Franzelin, I Kröker, S Oladyshkin, D Wittwar, G Santin, ... Zenodo, 2017 | 5 | 2017 |
| Hybrid stochastic Galerkin finite volumes for the diffusively corrected Lighthill-Whitham-Richards traffic model R Bürger, I Kröker Finite Volumes for Complex Applications VIII-Hyperbolic, Elliptic and …, 2017 | 5 | 2017 |
| The sparse polynomial chaos expansion: a fully bayesian approach with joint priors on the coefficients and global selection of terms PC Bürkner, I Kröker, S Oladyshkin, W Nowak arXiv preprint arXiv:2204.06043, 2022 | 3 | 2022 |
| The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory S Oladyshkin, T Praditia, I Kroeker, F Mohammadi, W Nowak, S Otte Neural Networks 166, Pages 85-104, 2023 | 2 | 2023 |
| Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the … R Kohlhaas, I Kröker, S Oladyshkin, W Nowak Computational Geosciences 27 (3), 369-389, 2023 | 2 | 2023 |
| The sparse Polynomial Chaos expansion: a fully Bayesian approach with joint priors on the coefficients and global selection of terms. arXiv preprint, 2022 PC Bürkner, I Kröker, S Oladyshkin, W Nowak URL http://arxiv. org/abs/2204.06043, 0 | 2 | |
| A computational approach to identifiability analysis for a model of the propagation and control of COVID-19 in Chile R Bürger, G Chowell, I Kröker, LY Lara-Díaz Journal of Biological Dynamics 17 (1), 2256774, 2023 | 1 | 2023 |
Christian RohdeUniversität Stuttgart在 mathematik.uni-stuttgart.de 的电子邮件经过验证
