Research Group Uncertainty Quantification

Sheme for propagation of uncertaintyExploiting mathematical models for simulation and forecasting requires not only sufficiently precise and affordable numerical solvers, but also the exact knowledge of all (important) model parameters. Particularly the latter is not always a given in practice. Natural variations of materials and their properties as well as random perturbations and forces call for a probabilistic approach to treat the limited knowledge about model coefficients. The resulting prediction should then sensibly quantify the corresponding range of plausible outputs.

Sheme for Bayesian inference within the workflow of uncertainty quantificationThis is exactly the focus of our research. We develop and analyze efficient methods for the propagation of uncertainty in complex computational models. To this end, we exploit, for instance, tools from high-dimensional approximation such as sparse grid collocation or deep neural networks. Moreover, we work on Bayesian approaches for inverse problems and data assimilation for differential equations, in particular, efficient and robust sampling methods for high-dimensional problems (e. g., Markov chain Monte Carlo, particle methods).


Principal Investigator

Juniorprofessor Dr. rer. nat. Björn Sprungk


Katja Hetze
Telefon: +49 3731 39−2798
Fax: +49 3731 39−3442
Email: Katja [dot] Hetzeatmath [dot] tu-freiberg [dot] de

Mailing Address

Technische Universität Bergakademie Freiberg
Fakultät für Mathematik und Informatik
D-09596 Freiberg


The Matriculation Ceremony in the Alte Mensa
With the ceremonial enrollment, TU Bergakademie Freiberg welcomes on 19. October their new students. As of the winter semester 2021/22 around 600 first-year students (as of 18. October; Registration deadline runs until the end of October). … weiterlesen

Two young scientists at the measuring platform
The first test campaign of the ESF-funded RoBiMo project is currently underway. Young researchers are testing the current status of the robot-based water monitoring system on Saxon reservoirs and collecting data that will then be evaluated with a view to climate change development. … weiterlesen

portaits of Ralf Hielscher und Daniel Hiller
With immediate effect, Professors Ralf Hielscher and Daniel Hiller occupy the professorship for Analytical Methods of Signal and Image Processing (Faculty 1) and the Heisenberg Professorship "Physics of Quantum Materials" (Faculty 2). … weiterlesen

four new Professors
Professors Sebastian Aland, Marcus Waurick, Christian Kupsch and Martin Gräbner are now Professors of Numerical Mathematics (Faculty 1), Partial Differential Equations (Faculty 1), Measurement, Sensor and Embedded Systems (Faculty 4) and Energy Process Engineering (Faculty 4). … weiterlesen

Syndicate content