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, Institute of Mathematics

Managing Director

Lydia Stolpmann, University of Potsdam, Institute of Mathematics

Funded by

DFG

Coordinated by

Upcoming Events

A tensor bidiagonalization method for singular value decomposition of third order tensors

Lothar Reichel, Kent State University 2.28.0.10810:15 - 11:45

The need to know a few singular triplets associated with the largest singular values of a third-order tensor arises in data compression and extraction. This paper describes a new method for their computation using the t-product. Methods for deter mining a couple of singular triplets associated with the smallest singular values also are presented. The proposed methods generalize available restarted Lanczos bidiagonalization methods for computing a few of the largest or smallest singular triplets of a matrix. The methods of this paper use Ritz and harmonic Ritz lateral slices to determine accurate approximations of the largest and smallest singular triplets, respectively. Computed examples show applications to data compression and face recognition. more ›

Latest Publications

Participating Institutions

imageHU BerlinTU IlmenauGFZ PotsdamTU BerlinWeierstraß-Institut Berlin