Model order reduction for Bayesian inference (A07*)

• Subject Area: Mathematics
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 318763901

Bayesian methods are popular for solving inverse problems. However, these methods involve sampling in high dimensional spaces or evaluating complex forward models, and thus tend to be computationally costly. This project will develop computationally efficient Bayesian inference algorithms based on model order reduction and dimension reduction methods. The effect of these methods on the accuracy of the resulting approximate posterior distribution will be studied.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1294: Data Assimilation – The Seamless Integration of Data and Models
• Applicant Institution: Universität Potsdam
• Project Heads: Professorin Dr. Melina Freitag, since 7/2021; Professor Dr. Han Cheng Lie, since 7/2021