Flow Radar Glyphs - Static Visualization of Unsteady Flow with Uncertainty. - Marcel Hlawatsch; Philipp C. Leube; Wolfgang Nowak; Daniel Weiskopf (IEEE Trans. Vis. Comput. Graph., 2011)

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

Towards optimal allocation of computer resources: Trade-offs between uncertainty quantification, discretization and model reduction. - Philipp C. Leube; Felipe P. J. de Barros; Wolfgang Nowak; Ram Rajagopal (Environ. Model. Softw., 2013)

Entropy-Based Experimental Design for Optimal Model Discrimination in the Geosciences. - Wolfgang Nowak; Anneli Guthke (Entropy, 2016)

The rocky road to extended simulation frameworks covering uncertainty, inversion, optimization and control. - Daniel Wirtz; Wolfgang Nowak (Environ. Model. Softw., 2017)

Sequential Design of Computer Experiments for the Solution of Bayesian Inverse Problems. - Michael Sinsbeck; Wolfgang Nowak (SIAM/ASA J. Uncertain. Quantification, 2017)

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)

Exploratory-Phase-Free Estimation of GP Hyperparameters in Sequential Design Methods - At the Example of Bayesian Inverse Problems. - Michael Sinsbeck; Marvin Höge; Wolfgang Nowak (Frontiers Artif. Intell., 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)

Sequential Design of Computer Experiments for the Computation of Bayesian Model Evidence. - Michael Sinsbeck; Emily Cooke; Wolfgang Nowak (SIAM/ASA J. Uncertain. Quantification, 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)

Combining Crop Modeling with Remote Sensing Data Using a Particle Filtering Technique to Produce Real-Time Forecasts of Winter Wheat Yields under Uncertain Boundary Conditions. - Hossein Zare; Tobias K. D. Weber; Joachim Ingwersen; Wolfgang Nowak; Sebastian Gayler; Thilo Streck (Remote. Sens., 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)

Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning. - Tim Schneider; Amin Totounferoush; Wolfgang Nowak; Steffen Staab (CoRR, 2023)

Using Surrogate Models and Data Assimilation for Efficient Mobile Simulations. - Christoph Dibak; Wolfgang Nowak; Frank Dürr; Kurt Rothermel (IEEE Trans. Mob. Comput., 2023)

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)

HydroStartML: A combined machine learning and physics-based approach to reduce hydrological model spin-up time. - Louisa Pawusch; Stefania Scheurer; Wolfgang Nowak; Reed Maxwell (CoRR, 2025)

Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models. - Stefania Scheurer; Philipp Reiser; Tim Brünnette; Wolfgang Nowak; Anneli Guthke; Paul-Christian Bürkner (CoRR, 2025)

Reliability sensitivity analysis with multiple failure domains based on an extended two-stage Markov chain Monte Carlo simulation. - Sinan Xiao; Wolfgang Nowak (Reliab. Eng. Syst. Saf., 2026)