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
Dr. Liv Heinecke, University of Potsdam, Department of Mathematics
Dr. Ester Mariucci will give a 2-part short course on Lévy Processes on the 31st of January and 07th of February 2019 from 14:15 to 17:45 The exercise...
The second annual SFB Spring School will take place from the 18th to the 22nd of March 2019 at the Ostseehotel Dierhagen.
Our invited guests for the...
This three-day workshop will review our state of knowledge on energy budgets and energy transfers in the climate system and how they are ...
This talk is concerned with the long time behavior of particle filters and Ensemble Kalman filters. These filters can be interpreted as mean field...more ›
A singular stochastic control problem typically describes the situation in which
an agent has to choose optimally an irreversible strategy in order to...
Part 1: Why do we add jumps to the Brownian motion?
In the first part of the mini course we will focus on jump processes with independent and...more ›
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, 10102-10111. 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 Cyclotron-Resonant Pitch-Angle Scattering Rates of Radiation Belt Electrons Due to EMIC Waves. Geophysical Research Letters, 45, 21-30. 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 (ECML-2018). Free PDF