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. Melina Freitag, University of Potsdam, Institute of Mathematics
Managing Director
Dr. Lydia Stolpmann, University of Potsdam, Institute of Mathematics
News
Sebastian Reich was invited speaker at Oden Institute - University of Austin in Texas

Sebastian Reich was invited speaker at Oden Institute, University of Austin in Texas, and gave a talk on "Generative modelling using Schrödinger… more ›
Melina Freitag is speaker of the CRC

The CRC 1294 started the new year with restructures in the leading team. Melina Freitag is speaker of the CRC and will be supported by Han Lie as vice… more ›
CRC Summer School 2024 in Boltenhagen

The CRC International Summer School 2024 took place from September 16th to 20th at the Lindner Hotels & Resorts Boltenhagen. Set in the beautiful… more ›
Upcoming Events
Women Network Lunch
12:00-14:00
Our SFB Women Networking Lunch provides an opportunity to discuss careers, opportunities, and ways to overcome challenges as women in a still…
more ›'Sparsity: The key to tackling large-scale least squares problems’ and 'Numerical linear algebra for data assimilation'
Jennifer Scott & Jemima Tabeart 2.27.1.0114:00-15:30
An additional colloquium with Jennifer Scott (STFC Rutherford Appleton Laboratory and The University of Reading, UK) and Jemima Tabeart (TU Eindhoven)…
more ›IRTG: "Professional Pathways" with Claudia Wiedemann
Claudia Wiedemann, AstraZeneca 14:00 - 16:00
We are delighted to announce the second session of our "Professional Pathways" career series, which invites professionals originally from mathematics…
more ›Latest Publications
Kim, J. W. and Reich, S. (2025): On forward-backward SDE approaches to continuous-time minimum variance estimation. In: Chapron, B., Crisan, D., Holm, D., Mémin, E., Coughlan, J.-L. (eds) Stochastic Transport in Upper Ocean Dynamics III. STUOD 2023. Mathematics of Planet Earth, vol 13. Springer, Cham., pp. 115-136. doi: 10.1007/978-3-031-70660-8
Reich, S. (2025): A particle-based Algorithm for Stochastic Optimal Control. In: Chapron, B., Crisan, D., Holm, D., Mémin, E., Coughlan, J.-L. (eds) Stochastic Transport in Upper Ocean Dynamics III. STUOD 2023. Mathematics of Planet Earth, vol 13. Springer, Cham., pp. 243-268. doi: 10.1007/978-3-031-70660-8
Pidstrigach, J., Marzouk, Y., Reich, S., and Wang, S. (2024). Infinite-Dimensional Diffusion Models. JMLR,Vol. 25, 1-52, arXiv 2302.10130