Fractured porous media are geometrically very complex. There are many competing model concepts to represent their structure in flow simulations; these models differ drastically in their level of geo¬metric detail and in their level of simplification and abstraction. Systematically choosing between these vastly different models, calibrating chosen models and specifying their predictive uncertainty is far from trivial. The goal of this project is to tackle the interlocked model-selection-and-calibration problem. Achieving this goal requires a list of algorithmic developments in the field of simulation-based Bayesian statistics.
Publications in Project B04
- Xu, T., Reuschen, S., Nowak, W., & Franssen, H.-J. H. (2020). Preconditioned Crank-Nicolson Markov Chain Monte Carlo Coupled With Parallel Tempering: An Efficient Method for Bayesian Inversion of Multi-Gaussian Log-Hydraulic Conductivity Fields. Water Resources Research, 56(8), Article 8. https://doi.org/10.1029/2020wr027110
- Höge, M., Guthke, A., & Nowak, W. (2020). Bayesian Model Weighting: The Many Faces of Model Averaging. Water, 12(2), 309. https://doi.org/10.3390/w12020309
- Reuschen, S., Xu, T., & Nowak, W. (2020). Bayesian inversion of hierarchical geostatistical models using a parallel-tempering sequential Gibbs MCMC. Advances in Water Resources, 141, 103614. https://doi.org/10.1016/j.advwatres.2020.103614
- Xiao, S., Reuschen, S., Köse, G., Oladyshkin, S., & Nowak, W. (2019). Estimation of small failure probabilities based on thermodynamic integration and parallel tempering. Mechanical Systems and Signal Processing, 133, 106248. https://doi.org/10.1016/j.ymssp.2019.106248