Welcome to the collaborative Research Center TRR 181 ”Energy transfers in Atmosphere and Ocean“
The seamless integration of large data sets into sophisticated computational models provides one of the central research challenges for the mathematical sciences in the 21st century. When the computational model is based on evolutionary equations and the data set is time-ordered, the process of combining models and data is called data assimilation. The assimilation of data into computational models serves a wide spectrum of purposes ranging from model calibration and model comparison all the way to the validation of novel model design principles.
The field of data assimilation has been largely driven by practitioners from meteorology, hydrology and oil reservoir exploration; but a theoretical foundation of the field is largely missing. Furthermore, many new applications are emerging from, for example, biology, medicine, and the neurosciences, which require novel data assimilation techniques. The goal of the proposed CRC is therefore twofold: First, to develop principled methodologies for data assimilation and, second, to demonstrate computational effectiveness and robustness through their implementation for established and novel data assimilation application areas.
While most current data assimilation algorithms are derived and analyzed from a Bayesian perspective, the CRC will view data assimilation from a general statistical inference perspective. Major challenges arise from the high-dimensionality of the inference problems, nonlinearity of the models and/or non-Gaussian statistics. Targeted application areas include the geoscience as well as emerging fields for data assimilation such as biophysics and cognitive neuroscience.
Speaker
Prof. Dr. Sebastian Reich, University of Potsdam, Department of Mathematics
Managing Director
Lydia Stolpmann, University of Potsdam, Department of Mathematics
News
Jana de Wiljes was awarded the title of Docent

Congratulations! We are proud that Jana de Wiljes, PI on A02, A03, B06 and B08, was awarded the title of Docent for Mathematics for Machine Learning… more ›
SFB Focus Retreat of A01 and B02

The focus retreat of project A01 (Markus Reiß, Wilhelm Stannat, Gregor Pasemann, Jan Szalankiewicz, and associated member Sascha Gaudlitz) and project… more ›
Registration open for the Symposium on Inverse Problems as part of the newly established Potsdam DA Days

The Symposium on Inverse Problems: From experimental data to models and back will take place in Potsdam (Campus Griebnitzsee) as part of the newly… more ›
Upcoming Events
Symmetry-informed model inference for active matter
Jörn Dunkel, Massachusetts Institute of Technology, USA Campus Golm, Building 28, Room 0.10810:15 - 11:15
Recent experimental advances enable high-resolution observations of biological and synthetic active matter across a wide range of length and time…
more ›5th Kálmán Lecture with Nicolas Chopin
Nicolas Chopin , ENSAE, Institut Polytechnique de Paris, France Campus Golm, Building 25, Room F0.0110:15-11:30
Nested cubing integration: how to get a O(N{-10}) error when you compute your favourite integral
This talk will explain why computing integrals…
more ›Jamboree 2022 - IRTG and associated members
This year Jamboree for all members of the IRTG as well as associated members will take place from 7th to 9th of Sepember at the KiEZ…
more ›Latest Publications
Pidstrigach, J. and Reich, S. (2022). Affine-invariant ensemble transform methods for logistic regression. Foundation of Computational Mathematics, 22, doi:10.10007/s10208-022-09550-2.
Molkenthin, C., Donner, C., Reich, S., Zöller, G., Hainzl, S., Holschneider, M. and Opper, M. (2022): GP-ETAS: Semiparametric Bayesian inference for the spatio-temporal Epidemic Type Aftershock Sequence model. Statistics and Computation, Vol. 32, 29. doi:10.1007/s11222-022-10085-3.
Huang, D.Z., Huang, J., Reich, S., and Stuart, A.M. (2022). Efficient derivative-free Bayesian inference for large-scale inverse problems. arXiv:2204.04386.