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Wolfgang Nowak
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- affiliation: University of Stuttgart, Germany
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2020 – today
- 2024
- [i7]Cosku Can Horuz, Matthias Karlbauer, Timothy Praditia, Sergey Oladyshkin, Wolfgang Nowak, Sebastian Otte:
Inferring Underwater Topography with FINN. CoRR abs/2408.10649 (2024) - 2023
- [j17]Cosku Can Horuz, Matthias Karlbauer, Timothy Praditia, Martin V. Butz
, Sergey Oladyshkin, Wolfgang Nowak, Sebastian Otte:
Physical Domain Reconstruction with Finite Volume Neural Networks. Appl. Artif. Intell. 37(1) (2023) - [j16]Paul-Christian Bürkner
A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms. J. Comput. Phys. 488: 112210 (2023) - [j15]Sergey Oladyshkin
The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory. Neural Networks 166: 85-104 (2023) - [j14]Christoph Dibak
Using Surrogate Models and Data Assimilation for Efficient Mobile Simulations. IEEE Trans. Mob. Comput. 22(3): 1856-1866 (2023) - [i6]Tim Schneider, Amin Totounferoush, Wolfgang Nowak, Steffen Staab:
Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning. CoRR abs/2306.08506 (2023) - [i5]Sergey Oladyshkin, Timothy Praditia, Ilja Kröker, Farid Mohammadi, Wolfgang Nowak, Sebastian Otte:
The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory. CoRR abs/2306.14753 (2023) - 2022
- [j13]Hossein Zare
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. Remote. Sens. 14(6): 1360 (2022) - [c2]Cosku Can Horuz
Infering Boundary Conditions in Finite Volume Neural Networks. ICANN (1) 2022: 538-549 - [c1]Matthias Karlbauer, Timothy Praditia, Sebastian Otte, Sergey Oladyshkin, Wolfgang Nowak, Martin V. Butz:
Composing Partial Differential Equations with Physics-Aware Neural Networks. ICML 2022: 10773-10801 - [d1]Timothy Praditia
Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network. DaRUS, 2022 - 2021
- [j12]Michael Sinsbeck, Emily Cooke, Wolfgang Nowak:
Sequential Design of Computer Experiments for the Computation of Bayesian Model Evidence. SIAM/ASA J. Uncertain. Quantification 9(1): 260-279 (2021) - [i4]Timothy Praditia, Matthias Karlbauer, Sebastian Otte, Sergey Oladyshkin, Martin V. Butz, Wolfgang Nowak:
Finite Volume Neural Network: Modeling Subsurface Contaminant Transport. CoRR abs/2104.06010 (2021) - [i3]Matthias Karlbauer, Timothy Praditia, Sebastian Otte, Sergey Oladyshkin, Wolfgang Nowak, Martin V. Butz:
Composing Partial Differential Equations with Physics-Aware Neural Networks. CoRR abs/2111.11798 (2021) - 2020
- [j11]Sergey Oladyshkin
Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory. Entropy 22(8): 890 (2020) - [j10]Michael Sinsbeck, Marvin Höge, Wolfgang Nowak:
Exploratory-Phase-Free Estimation of GP Hyperparameters in Sequential Design Methods - At the Example of Bayesian Inverse Problems. Frontiers Artif. Intell. 3: 52 (2020) - [j9]Sinan Xiao
Reliability analysis with stratified importance sampling based on adaptive Kriging. Reliab. Eng. Syst. Saf. 197: 106852 (2020)
2010 – 2019
- 2019
- [j8]Sergey Oladyshkin
The Connection between Bayesian Inference and Information Theory for Model Selection, Information Gain and Experimental Design. Entropy 21(11): 1081 (2019) - [i2]Christoph Dibak, Wolfgang Nowak, Frank Dürr, Kurt Rothermel:
Using Surrogate Models and Data Assimilation for Efficient Mobile Simulations. CoRR abs/1911.10344 (2019) - 2018
- [j7]Sergey Oladyshkin
Incomplete statistical information limits the utility of high-order polynomial chaos expansions. Reliab. Eng. Syst. Saf. 169: 137-148 (2018) - [i1]Markus Köppel, Fabian Franzelin, Ilja Kröker, Sergey Oladyshkin, Gabriele Santin, Dominik Wittwar, Andrea Barth, Bernard Haasdonk, Wolfgang Nowak, Dirk Pflüger, Christian Rohde:
Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. CoRR abs/1802.03064 (2018) - 2017
- [j6]Daniel Wirtz
The rocky road to extended simulation frameworks covering uncertainty, inversion, optimization and control. Environ. Model. Softw. 93: 180-192 (2017) - [j5]Michael Sinsbeck, Wolfgang Nowak
Sequential Design of Computer Experiments for the Solution of Bayesian Inverse Problems. SIAM/ASA J. Uncertain. Quantification 5(1): 640-664 (2017) - 2016
- [j4]Wolfgang Nowak
Entropy-Based Experimental Design for Optimal Model Discrimination in the Geosciences. Entropy 18(11): 409 (2016) - 2013
- [j3]Philipp C. Leube, Felipe P. J. de Barros, Wolfgang Nowak
Towards optimal allocation of computer resources: Trade-offs between uncertainty quantification, discretization and model reduction. Environ. Model. Softw. 50: 97-107 (2013) - 2012
- [j2]Sergey Oladyshkin
Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion. Reliab. Eng. Syst. Saf. 106: 179-190 (2012) - 2011
- [j1]Marcel Hlawatsch
Flow Radar Glyphs - Static Visualization of Unsteady Flow with Uncertainty. IEEE Trans. Vis. Comput. Graph. 17(12): 1949-1958 (2011)
Coauthor Index
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