Sebastian Reuschen

Xu T, Xiao S, Reuschen S, Wildt N, Franssen HJH, Nowak W. Towards a community-wide effort for benchmarking in subsurface hydrological inversion: benchmarking cases, high-fidelity reference solutions, procedure and a first comparison. Hydrology and Earth System Sciences.

Xiao S, Xu T, Reuschen S, Nowak W, Franssen HJH. Bayesian inversion of multi-Gaussian log-conductivity fields with uncertain hyperparameters: an extension of preconditioned Crank-Nicolson Markov chain Monte Carlo with parallel tempering. Water Resources Research. 2021;57:e2021WR030313.

Reuschen S, Guthke A, Nowak W. The Four Ways to Consider Measurement Noise in Bayesian Model Selection - And Which One to Choose. Water Resources Research. 2021;57:e2021WR030391.

Reuschen S. Bayesian inversion and model selection of heterogeneities in geostatistical subsurface modeling [Internet] [Dissertation]. Mitteilungen / Institut für Wasser- und Umweltsystemmodellierung, Universität Stuttgart. [Stuttgart]: Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung; 2021. (Mitteilungen / Institut für Wasser- und Umweltsystemmodellierung, Universität Stuttgart). Verfügbar unter: http://elib.uni-stuttgart.de/handle/11682/12030

Reuschen S, Jobst F, Nowak W. Efficient discretization-independent Bayesian inversion of high-dimensional multi-Gaussian priors using a hybrid MCMC. Water Resources Research. 2021;57(8):e2021WR030051.

Xu T, Reuschen S, Nowak W, Franssen HJH. 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. 2020;56(8):e2020WR027110.

Reuschen S, Xu T, Nowak W. Bayesian inversion of hierarchical geostatistical models using a parallel-tempering sequential Gibbs MCMC algorithm. Advances in Water Resources. 2020;141:103614.

Xiao S, Reuschen S, Köse G, Oladyshkin S, Nowak W. Estimation of small failure probabilities based on thermodynamic integration and parallel tempering. Mechanical Systems and Signal Processing. 2019;133:106248.

Bode F, Reed P, Reuschen S, Nowak W. Search Space Representation and Reduction Methods to Enhance Multi-Objective Water Supply Monitoring Design. Water Resources Research. 2019;55(3):2257–78.

Bode F, Nowak W, Reed PM, Reuschen S. Putting Man in the Machine: Exploiting Expertise to Enhance Multiobjective Design of Water Supply Monitoring Network. In: Fall Meeting 2016, Abstract: H51A-1413. San Francisco, CA, USA: American Geophysical Union (AGU); 2016. (Fall Meeting 2016, Abstract: H51A-1413).

Bode F, Reuschen S, Nowak W. Never Use the Complete Search Space: A Concept to Enhance the Optimization Procedure for Monitoring Networks. In: Fall Meeting 2015, Abstract: IN11B-1774. San Francisco, CA, USA: American Geophysical Union (AGU); 2015. (Fall Meeting 2015, Abstract: IN11B-1774).