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Towards a community-wide effort for benchmarking in subsurface hydrological inversion: benchmarking cases, high-fidelity reference solutions, procedure and a first comparison

T. Xu, S. Xiao, S. Reuschen, N. Wildt, H. Franssen, und W. Nowak. Hydrology and Earth System Sciences, (2024 (submitted))

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Sebastian Friedl University of Stuttgart

Communication extension for cloud-based machine control of simulated robot processesF. Kretschmer, S. Friedl, A. Lechler, und A. Verl. 2016 IEEE International Conference on Industrial Technology (ICIT), Seite 54-58. (März 2016)

Sebastian Brandhofer University of Stuttgart

Sebastian Haunss

Dr. Sebastian Rojek University of Stuttgart

Sebastian Lippold University of Stuttgart

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Towards a community-wide effort for benchmarking in subsurface hydrological inversion: benchmarking cases, high-fidelity reference solutions, procedure and a first comparisonT. Xu, S. Xiao, S. Reuschen, N. Wildt, H. Franssen, und W. Nowak. Hydrology and Earth System Sciences, (2024 (submitted))Hydraulically induced fracturing in heterogeneous porous media using a TPM-phase-ﬁeld model and geostatisticsA. Wagner, A. Sonntag, S. Reuschen, W. Nowak, und W. Ehlers. Proceedings in Applied Mathematics and Mechanics, (2023)Search Space Representation and Reduction Methods to Enhance Multiobjective Water Supply Monitoring DesignF. Bode, P. Reed, S. Reuschen, und W. Nowak. Water Resources Research, 55 (3): 2257-2278 (2019)Code and data of Bayesian inversion of hierarchical geostatistical models using a parallel-tempering sequential Gibbs MCMCS. Reuschen, T. Xu, und W. Nowak. Dataset, (2020)Related to: 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. doi: 10.1016/j.advwatres.2020.103614.Bayesian Inversion of Multi-Gaussian Log-Conductivity Fields With Uncertain Hyperparameters : An Extension of Preconditioned Crank-Nicolson Markov Chain Monte Carlo With Parallel TemperingS. Xiao, T. Xu, S. Reuschen, W. Nowak, und H. Hendricks-Franssen. Water resources research, 57 (9): e2021WR030313 (2021)Bayesian inversion of hierarchical geostatistical models using a parallel-tempering sequential Gibbs MCMCS. Reuschen, T. Xu, und W. Nowak. Advances in water resources, 141 (July): 103614 (2020)Preconditioned Crank-Nicolson Markov Chain Monte Carlo Coupled With Parallel Tempering : An Efficient Method for Bayesian Inversion of Multi-Gaussian Log-Hydraulic Conductivity FieldsT. Xu, S. Reuschen, W. Nowak, und H. Hendricks Franssen. Water resources research, 56 (8): e2020WR027110 (2020)The Four Ways to Consider Measurement Noise in Bayesian Model Selection : And Which One to ChooseS. Reuschen, A. Guthke, und W. Nowak. Water resources research, 57 (11): e2021WR030391 (2021)Estimation of small failure probabilities based on thermodynamic integration and parallel temperingS. Xiao, S. Reuschen, G. Köse, S. Oladyshkin, und W. Nowak. Mechanical Systems and Signal Processing, (2019)Bayesian inversion and model selection of heterogeneities in geostatistical subsurface modelingS. Reuschen. Universität Stuttgart, Stuttgart, Dissertation, (2021)