Contact
Universitätsstraße 32
70569 Stuttgart
Room: 227c
Office Hours
Consultation by appointment
Subject
- Model evaluation
- Uncertainty and error quantification
- Stochastic simulation
- Information-theoretic concepts
- Hybrid machine-learning/simulation approaches
- Hydro(geo)logical modelling
2024
- A. Guthke, P. L. Reiser, and P. Bürkner, “Data-Integrated Training of Surrogates as a Bayesian Hybrid Modeling Strategy,” 2024.
- P. Reiser, P.-C. Bürkner, and A. Guthke, “Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy.” 2024. [Online]. Available: https://arxiv.org/abs/2412.11875
- A. Guthke, P. Reiser, and P.-C. Bürkner, “Quantifying Uncertainty in Surrogate-based Bayesian Inference,” 2024.
- M. Á. Chaves, E. A. Espinoza, U. Ehret, and A. Guthke, “Evaluating physics-based representations of hydrological systems through hybrid models and information theory,” 2024.
- A. Guthke, “Verbesserte Unsicherheitsabschätzung für (fehlerbehaftete) Grundwassermodelle,” in 29. Tagung der Fachsektion Hydrogeologie e.V. in der DGGV e.V., 2024.
- M. Álvarez Chaves, H. V. Gupta, U. Ehret, and A. Guthke, “On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data,” Entropy, vol. 26, Art. no. 5, 2024, doi: 10.3390/e26050387.
- T. Wöhling, A. O. Crespo Delgadillo, M. Kraft, and A. Guthke, “Comparing Physics-based, Conceptual and Machine-Learning Models to Predict Groundwater Levels by Bayesian Model Averaging,” Groundwater, vol. in revision, 2024.
- H.-F. Hsueh, A. Guthke, T. Wöhling, and W. Nowak, “Optimized Predictive Coverage by Averaging Time-Windowed Bayesian Distributions,” Water resources research, vol. 60, Art. no. 5, 2024, doi: 10.1029/2022WR033280.
2023
- M. Álvarez Chaves, A. Guthke, U. Ehret, and H. Gupta, “UNITE: A toolbox for unified diagnostic evaluation of physics-based, data-driven and hybrid models based on information theory,” in EGU General Assembly Conference Abstracts, 2023, pp. EGU––4039.
- A. Guthke, “Modified Bayesian Calibration Approaches to Tackle the Erroneous-Model Problem,” in AGU23, AGU, 2023.
- J. T. White, M. N. Fienen, C. R. Moore, and A. Guthke, “Editorial: Rapid, reproducible, and robust environmental modeling for decision support: worked examples and open-source software tools,” Frontiers in Earth Science, vol. 11, 2023, doi: 10.3389/feart.2023.1260581.
- F. Ejaz, A. Guthke, T. Wöhling, and W. Nowak, “Comprehensive uncertainty analysis for surface water and groundwater projections under climate change based on a lumped geo-hydrological model,” Journal of Hydrology, vol. 626, 2023, doi: https://doi.org/10.1016/j.jhydrol.2023.130323.
- I. Banerjee, P. Walter, A. Guthke, K. G. Mumford, and W. Nowak, “The method of forced probabilities : a computation trick for Bayesian model evidence,” Computational Geosciences, vol. 27, Art. no. 1, 2023, doi: 10.1007/s10596-022-10179-x.
- I. Banerjee, A. Guthke, C. J. Van De Ven, K. G. Mumford, and W. Nowak, “Comparison of Four Competing Invasion Percolation Models for Gas Flow in Porous Media,” Authorea Preprints, 2023.
- P. Reiser, J. E. Aguilar, A. Guthke, and P.-C. Bürkner, “Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference,” arXiv preprint arXiv:2312.05153, vol. (submitted), 2023.
2022
- A. Guthke, H.-F. Hsueh, T. Wöhling, and W. Nowak, “Bayesian updating despite model errors? A sliding time-window approach to rescue,” in EGU General Assembly Conference Abstracts, 2022, pp. EGU22––12525.
- M. Viswanathan, T. K. Weber, and A. Guthke, “An alternative strategy for combining likelihood values in Bayesian calibration to improve model predictions,” in EGU General Assembly Conference Abstracts, 2022, pp. EGU22––1210.
- A. Schäfer Rodrigues Silva et al., “Diagnosing Similarities in Probabilistic Multi-Model Ensembles - an Application to Soil-Plant-Growth-Modeling,” Modeling Earth Systems and Environment, vol. 8, pp. 5143–5175, 2022, doi: 10.1007/s40808-022-01427-1.
- H.-F. Hsueh, A. Guthke, T. Wöhling, and W. Nowak, “Diagnosis of Model Errors With a Sliding Time-Window Bayesian Analysis,” Water resources research, vol. 58, Art. no. 2, 2022, doi: 10.1029/2021WR030590.
2021
- I. Banerjee, A. Guthke, C. Van De Ven, K. Mumford, and W. Nowak, “A Quantitative Method to Evaluate the Performance of Competing (Stochastic) Invasion Percolation Models under Different Gas-Flow Regimes,” in AGU Fall Meeting Abstracts, 2021, pp. H12E––05.
- S. Reuschen, A. Guthke, and W. Nowak, “The Four Ways to Consider Measurement Noise in Bayesian Model Selection - And Which One to Choose,” Water resources research, vol. 57, Art. no. 11, 2021, doi: 10.1029/2021WR030391.
- I. Banerjee, A. Guthke, C. J. C. Van de Ven, K. G. Mumford, and W. Nowak, “Overcoming the model-data-fit problem in porous media : A Quantitative Method to Compare Invasion-Percolation Models to High-Resolution Data,” Water Resources Research, vol. 57, Art. no. 7, 2021.
2020
- A. Guthke et al., “A unified framework for quantitative interdisciplinary flood risk assessment,” online: AGU Fall Meeting 2020, Dec. 2020.
- M. Höge, A. Guthke, and W. Nowak, “Bayesian Model Weighting: The Many Faces of Model Averaging,” Water, vol. 12, Art. no. 2, 2020, doi: 10.3390/w12020309.
- A. Schäfer Rodrigues Silva, A. Guthke, M. Höge, O. A. Cirpka, and W. Nowak, “Strategies for simplifying reactive transport models: A Bayesian model comparison,” Water Resources Research, vol. 56, Art. no. 11, 2020.
2019
- D. F. Motavita, R. Chow, A. Guthke, and W. Nowak, “The comprehensive differential split-sample test: A stress-test for hydrological model robustness under climate variability,” Journal of Hydrology, vol. 573, pp. 501–515, 2019, doi: https://doi.org/10.1016/j.jhydrol.2019.03.054.
- M. Höge, A. Guthke, and W. Nowak, “The hydrologist’s guide to Bayesian model selection, averaging and combination,” Journal of Hydrology, vol. 572, pp. 96–107, 2019, doi: 10.1016/j.jhydrol.2019.01.072.
2018
- P. Darscheid, A. Guthke, and U. Ehret, “A maximum-entropy method to estimate discrete distributions from samples ensuring nonzero probabilities,” Entropy, vol. 20, Art. no. 8, 2018.
- F. Mohammadi, R. Kopmann, A. Guthke, S. Oladyshkin, and W. Nowak, “Bayesian selection of hydro-morphodynamic models under computational time constraints,” Advances in Water Resources, vol. 117, pp. 53–64, 2018.
2017
- A. Guthke, “Defensible Model Complexity: A Call for Data-Based and Goal-Oriented Model Choice,” Groundwater, vol. 55, Art. no. 5, 2017, doi: 10.1111/gwat.12554.
2016
- W. Nowak and A. Guthke, “Entropy-based experimental design for optimal model discrimination in the geosciences,” Entropy, vol. 18, Art. no. 11, 2016.
- O. Lötgering-Lin, A. Schöniger, W. Nowak, and J. Groß, “Bayesian Model Selection Helps To Choose Objectively between Thermodynamic Models: A Demonstration of Selecting a Viscosity Model Based on Entropy Scaling,” Industrial & engineering chemistry research, vol. 55, Art. no. 38, 2016, doi: 10.1021/acs.iecr.6b02671.
2015
- T. Wöhling, A. Schöniger, S. Gayler, and W. Nowak, “Bayesian model averaging to explore the worth of data for soil-plant model selection and prediction,” Water resources research, vol. 51, Art. no. 4, 2015, doi: 10.1002/2014WR016292.
- A. Schöniger, T. Wöhling, and W. Nowak, “A statistical concept to assess the uncertainty in Bayesian model weights and its impact on model ranking,” Water resources research, vol. 51, Art. no. 9, 2015, doi: 10.1002/2015WR016918.
- A. Schöniger, W. Illman, T. Wöhling, and W. Nowak, “Finding the Right Balance Between Groundwater Model Complexity and Experimental Effort via Bayesian Model Selection,” Journal of Hydrology, vol. 531, Art. no. 1, 2015, doi: 10.1016/j.jhydrol.2015.07.047.
2014
- A. Schöniger, T. Wöhling, L. Samaniego, and W. Nowak, “Model selection on solid ground: rigorous comparison of nine ways to evaluate Bayesian evidence,” Water Resources Research, vol. 50, Art. no. 12, 2014, doi: 10.1002/2014WR016062.
2012
- A. Schöniger, W. Nowak, and H. J. H. Franssen, “Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography,” Water Resources Research, vol. 48, Art. no. W04502, 2012, doi: 10.1029/2011WR010462 (was the top cited 2012 WRR article in 2013).
- Since Oct. 2021: Independent Junior Research Group Leader with SimTech, University of Stuttgart/Germany
- 2017 - 2021: Postdoctoral researcher with the Department of Stochastic Simulation and Safety Research for Hydrosystems (IWS / LS³), University of Stuttgart/Germany
- 2015 - 2016: Postdoctoral researcher with the Center for Applied Geoscience (ZAG), University of Tübingen/Germany
- 2012 - 2015: Doctoral researcher within the International Research Training Group "Integrated Hydrosystem Modelling" (GRK 1829), University of Tübingen/Germany and University of Waterloo/ON, Canada
- 2010 - 2018: Environmental consultant with BoSS Consult Gmbh, Stuttgart/Germany
- 2004 - 2010: Studies of environmental engineering (Dipl.-Ing.) with a major in hydrosystem modelling, University of Stuttgart/Germany
EXC
- Participating Researcher
- Junior Research Group Leader "Statistical Model-Data Integration"