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 timeordered, 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 highdimensionality of the inference problems, nonlinearity of the models and/or nonGaussian 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
Coordinator
Dr. Liv Heinecke, University of Potsdam, Department of Mathematics
News
RingVL 'Interdisciplinary Mathematics' this term
Interested in what we do in the SFB 1294 and what concepts are behind the research in the different projects? Our RingVL will start this week, with...
more ›First SFB 1294 Jamboree
From the 19th until 21st of September the PhD students and PostDocs of the SFB1294 assembled at Kiez Bollmannsruh in Brandenburg for their first...
more ›Annual Kalman Lecture established
We are pleased to announce that the 1st Kalman Lecture will take place on the 24th of August, 2018.
The Kalman Lecture will be established as an...
more ›Upcoming Events
Hamiltonian Monte Carlo methods on Hilbert spaces
Jakiw Pidstrigach, University of Bonn 2.9.0.14 10:15  11:00
When sampling measures on Hilbert spaces one option is to first discretize the space and then apply standard Markov Chain Monte Carlo methods. This...
more ›A Causal approach to springtosummer climate variability in the Southern Hemisphere
Elena Saggioro, University of Reading, UK 2.9.0.14 11:00  11:45
The coupling between stratospheric and tropospheric dynamics is currently a topic of major interest [1,2]. In the context of the Southern...
more ›Dances with Drones: Using Google’s TFLite for Autonomous Control of Aerial Drones by Gesture Recognition
Erin Linebarger, University of Utah (US) and SFB 1294, University of Potsdam 2.9.0.14 10:15  11:45
Scientific app development for implementing robotics controllers and machine learning algorithms has become much easier with the introduction of tools...
more ›Latest Publications

N. A. Aseev, Y. Y. Shprits, A. Y. Drozdov, A. C. Kellerman, M. E. Usanova, D. Wang, I. S. Zhelavskaya (2017). Signatures of Ultrarelativistic Electron Loss in the Heart of the Outer Radiation Belt Measured by Van Allen Probes. Journal of Geophysical Research, 122, 1010210111. doi: 10.1002/2017JA024485

B. Ni, X. Cao, Y. Y. Shprits, D. Summers, X. Gu, S. Fu, Y. Lou (2018). Hot Plasma Effects on the CyclotronResonant PitchAngle Scattering Rates of Radiation Belt Electrons Due to EMIC Waves. Geophysical Research Letters, 45, 2130. doi: 10.1002/2017GL07602

S. Makowski, L. Jäger, A. Abdelwahab, N. Landwehr and T. Scheffer (2018). A discriminative model for identifying readers and assessing text comprehension from eye movements. Proceedings of the European Conference on Machine Learning (ECML2018). Free PDF