Publications

Reiser, P., Bürkner, P.-C., & Guthke, A. (2024). Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy. https://arxiv.org/abs/2412.11875

Reiser, P., Aguilar, J. E., Guthke, A., & Bürkner, P.-C. (2024). Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. Statistics and Computing, in revision.

Álvarez Chaves, M., Gupta, H. V., Ehret, U., & Guthke, A. (2024). On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data. Entropy, 26, Article 5. https://doi.org/10.3390/e26050387

Hsueh, H.-F., Guthke, A., Wöhling, T., & Nowak, W. (2024). Optimized Predictive Coverage by Averaging Time-Windowed Bayesian Distributions. Water resources research, 60, Article 5. https://doi.org/10.1029/2022WR033280

Wöhling, T., Crespo Delgadillo, A. O., Kraft, M., & Guthke, A. (2024). Comparing Physics-based, Conceptual and Machine-Learning Models to Predict Groundwater Levels by Bayesian Model Averaging. Groundwater, in revision.

Ejaz, F., Guthke, A., Wöhling, T., & Nowak, W. (2023). Comprehensive uncertainty analysis for surface water and groundwater projections under climate change based on a lumped geo-hydrological model. Journal of Hydrology, 626. https://doi.org/10.1016/j.jhydrol.2023.130323

Banerjee, I., Guthke, A., Van De Ven, C. J., Mumford, K. G., & Nowak, W. (2023). Comparison of Four Competing Invasion Percolation Models for Gas Flow in Porous Media. Authorea Preprints.

White, J. T., Fienen, M. N., Moore, C. R., & Guthke, A. (2023). Editorial: Rapid, reproducible, and robust environmental modeling for decision support: worked examples and open-source software tools. Frontiers in Earth Science, 11. https://doi.org/10.3389/feart.2023.1260581

Banerjee, I., Walter, P., Guthke, A., Mumford, K. G., & Nowak, W. (2023). The method of forced probabilities : a computation trick for Bayesian model evidence. Computational Geosciences, 27, Article 1. https://doi.org/10.1007/s10596-022-10179-x

Hsueh, H.-F., Guthke, A., Wöhling, T., & Nowak, W. (2022). Diagnosis of Model Errors With a Sliding Time-Window Bayesian Analysis. Water resources research, 58, Article 2. https://doi.org/10.1029/2021WR030590

Schäfer Rodrigues Silva, A., Weber, T. K., Gayler, S., Guthke, A., Höge, M., Streck, T., & Nowak, W. (2022). Diagnosing Similarities in Probabilistic Multi-Model Ensembles - an Application to Soil-Plant-Growth-Modeling. Modeling Earth Systems and Environment, 8, 5143–5175. https://doi.org/10.1007/s40808-022-01427-1

Guthke, A., Reiser, P. L., & Bürkner, P. (2024). Data-Integrated Training of Surrogates as a Bayesian Hybrid Modeling Strategy.

Chaves, M. Á., Espinoza, E. A., Ehret, U., & Guthke, A. (2024). Evaluating physics-based representations of hydrological systems through hybrid models and information theory. EGU General Assembly Conference Abstracts, EGU24.

Guthke, A. (2024). Verbesserte Unsicherheitsabschätzung für (fehlerbehaftete) Grundwassermodelle. 29. Tagung Der Fachsektion Hydrogeologie E.V. In Der DGGV E.V.

Chaves, M. Á., Guthke, A., Ehret, U., & Gupta, H. (2023). UNITE: A Toolbox for Unified Diagnostic Evaluation of Physics-based, Data-driven and Hybrid Models based on Information Theory. EGU General Assembly Conference Abstracts, EGU23.

Guthke, A. (2023). Modified Bayesian Calibration Approaches to Tackle the Erroneous-Model Problem. AGU Fall Meeting Conference Abstracts, AGU23.

Guthke, A., Hsueh, H.-F., Wöhling, T., & Nowak, W. (2022). Bayesian updating despite model errors? A sliding time-window approach to rescue. EGU General Assembly Conference Abstracts, EGU22–12525.

Viswanathan, M., Weber, T. K., & Guthke, A. (2022). An alternative strategy for combining likelihood values in Bayesian calibration to improve model predictions. EGU General Assembly Conference Abstracts, EGU22–1210.

Guthke, A., Reiser, P., & Bürkner, P.-C. (2024). Quantifying Uncertainty in Surrogate-based Bayesian Inference. EGU General Assembly Conference Abstracts, EGU24.

Reiser, P., Aguilar, J. E., Guthke, A., & Bürkner, P.-C. (2024). Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. SIAM Conference on Uncertainty Quantification (UQ24).

Reiser, P., Aguilar, J. E., Guthke, A., & Bürkner, P.-C. (2023). Quantifying Uncertainty in Surrogate-based Bayesian Inference. International Conference on Data-Integrated Simulation Science (SimTech2023).

Reiser, P. (2023). Quantifying Uncertainty in Surrogate-based Bayesian Inference. Bayes Comp 2023.