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.
Prof. Dr. Sebastian Reich, University of Potsdam, Department of Mathematics
Liv Heinecke, University of Potsdam, Department of Mathematics
The second SFB1294 Roboter Coder Dojo took place on Sunday, the 04.03., and even more kids attended with their parents than last time. The mentors of...
The first Roboter Coder Dojo took place last Sunday and was a lot of fun for everybody involved. Around 15 kids from age 5 to 14, accompanied by their...
The SFB 1294 was one of the co-organizers for the Geo.X Career Day 2017. The theme of this year’s career day is a highly relevant one for many – but...
Im Rahmen des Zukunftstages sollen Schüler der 7. bis 10. Klasse eine Chance bekommen Einblicke in geschlechteruntypische Berufe zu erhalten. Dazu...more ›
We consider a stochastic Hodgkin-Huxley model where dendritic input -modelled as an autonomous SDE which depends on a deterministic T-periodic signal...more ›
In stochastic dynamics inspired by Statistical Mechanics the interaction between different particles, or agents, is usually ...
Schütt, Heiko H., Rothkegel, Lars O. M., Trukenbrod, Hans A., Reich, Sebastian, Wichmann, Felix A., Engbert, Ralf (2017) Likelihood-based parameter estimation and comparison of dynamical cognitive models. Psychological Review, Vol 124(4), Jul 2017, 505-524 http://dx.doi.org/10.1037/rev0000068
Angwenyi David, Jana de Wiljes, and Sebastian Reich (2017) Interacting particle filters for simultaneous state and parameter estimation. arXiv:1709.09199
Amirhossein Taghvaei, Jana de Wiljes, Prashant G. Mehta, and Sebastian Reich (2017) Kalman filter and its modern extensions for the continuous-time nonlinear filtering problem ASME Journal of Dynamical Systems, Measurement, and Control, published online August 31, 2017, arXiv:1702.07241