Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion. - Sergey Oladyshkin; Wolfgang Nowak (Reliab. Eng. Syst. Saf., 2012)

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)

Incomplete statistical information limits the utility of high-order polynomial chaos expansions. - Sergey Oladyshkin; Wolfgang Nowak (Reliab. Eng. Syst. Saf., 2018)

The Connection between Bayesian Inference and Information Theory for Model Selection, Information Gain and Experimental Design. - Sergey Oladyshkin; Wolfgang Nowak (Entropy, 2019)

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

Reliability analysis with stratified importance sampling based on adaptive Kriging. - Sinan Xiao; Sergey Oladyshkin; Wolfgang Nowak (Reliab. Eng. Syst. Saf., 2020)

Finite Volume Neural Network: Modeling Subsurface Contaminant Transport. - Timothy Praditia; Matthias Karlbauer; Sebastian Otte; Sergey Oladyshkin; Martin V. Butz; Wolfgang Nowak (CoRR, 2021)

Infering Boundary Conditions in Finite Volume Neural Networks. - Cosku Can Horuz; Matthias Karlbauer; Timothy Praditia; Martin V. Butz; Sergey Oladyshkin; Wolfgang Nowak; Sebastian Otte (ICANN (1), 2022)

Composing Partial Differential Equations with Physics-Aware Neural Networks. - Matthias Karlbauer; Timothy Praditia; Sebastian Otte; Sergey Oladyshkin; Wolfgang Nowak; Martin V. Butz (ICML, 2022)

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

Finite Volume Neural Network. - Timothy Praditia; Matthias Karlbauer; Sebastian Otte; Sergey Oladyshkin; Martin V. Butz; Wolfang Nowak (Zenodo, 2022)

Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network. - Timothy Praditia; Matthias Karlbauer; Sebastian Otte; Sergey Oladyshkin; Martin V. Butz; Wolfgang Nowak (DaRUS, 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)

Physical Domain Reconstruction with Finite Volume Neural Networks. - Cosku Can Horuz; Matthias Karlbauer; Timothy Praditia; Martin V. Butz; Sergey Oladyshkin; Wolfgang Nowak; Sebastian Otte (Appl. Artif. Intell., 2023)

Inferring Underwater Topography with FINN. - Cosku Can Horuz; Matthias Karlbauer; Timothy Praditia; Sergey Oladyshkin; Wolfgang Nowak; Sebastian Otte (CoRR, 2024)