Computational uncertainty quantification for a clarifier-thickener model with several random perturbations: A hybrid stochastic Galerkin approach. - Andrea Barth; Raimund Bürger; Ilja Kröker; Christian Rohde (Comput. Chem. Eng., 2016)

Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. - Markus Köppel; Fabian Franzelin; Ilja Kröker; Sergey Oladyshkin; Gabriele Santin; Dominik Wittwar; Andrea Barth; Bernard Haasdonk; Wolfgang Nowak; Dirk Pflüger; Christian Rohde (CoRR, 2018)

Computational uncertainty quantification for some strongly degenerate parabolic convection-diffusion equations. - Raimund Bürger; Ilja Kröker (J. Comput. Appl. Math., 2019)

Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory. - Sergey Oladyshkin; Farid Mohammadi; Ilja Kröker; Wolfgang Nowak (Entropy, 2020)

Arbitrary multi-resolution multi-wavelet-based polynomial chaos expansion for data-driven uncertainty quantification. - Ilja Kröker; Sergey Oladyshkin (Reliab. Eng. Syst. Saf., 2022)

A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms. - Paul-Christian Bürkner; Ilja Kröker; Sergey Oladyshkin; Wolfgang Nowak (J. Comput. Phys., 2023)

The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory. - Sergey Oladyshkin; Timothy Praditia; Ilja Kröker; Farid Mohammadi; Wolfgang Nowak; Sebastian Otte (Neural Networks, 2023)