Research Group Scientific Computing & Uncertainty Quantification
The group of Prof. Ullmann focuses on Uncertainty Quantification for PDE-based models. The goal of our work is the design and analysis of efficient algorithms and estimators for PDEs with random inputs. To this end we use tools from Numerical Analysis, Data Science and Computational Science and Engineering.
- Uncertainty analysis, uncertainty propagation
- Multilevel estimators
- Reliability analysis and rare events
- Statistical (Bayesian) inverse problems
- Model-based machine learning
- In 2023 Jonas Latz wins the SIGEST Award with the paper Bayesian Inverse Problems are Usually Well-Posed. Congratulations!
- Since 2022 Prof. Ullmann is Associate Editor of the SIAM Journal on Scientific Computing and the SIAM/ASA Journal on Uncertainty Quantification.
- In 2021-2022 Prof. Ullmann is elected Vice Chair of the SIAM Activity Group on Uncertainty Quantification.
- In 2020 Jonas Latz wins the SIAM Student Paper Prize. Congratulations!
We are always looking for motivated students who want to write a thesis or carry out a term project in Uncertainty Quantification, Scientific Computing or Numerical Analysis. We are happy to discuss possible topics according to your interest and knowledge. As prerequisite you should have completed a course (e.g. MA5348, MA9804, MA9803) or seminar (Bachelor, Master) offered by the group (see teaching page). Possible topics are:
- Pre-smoothing and Multilevel Monte Carlo for rare event estimation
- Projection-based surrogate models for Bayesian reliability updating
- Uncertainty Quantification in dynamical systems
- List of supervised theses