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
News
Jakiw Pidstrigach successfully defended his PhD thesis
Congratulations to Jakiw Pidstrigach who successfully defended his PhD thesis on "Diverse Paths, One Destination A Study of Modern Sampling Methods"… more ›
Josie König successfully applied for a Fulbright Grant
Congratulations to Josie König (PhD candidate in project A07) who succesfully applied for a Fulbright Germany Grant. With this Grant, Josie will… more ›
Focus Retreat on Hiddensee 2024
The 3rd Focus Retreat on Hiddensee took place from 4th to 8th of April, 2024. Sebastian Reich (Z01, A02, A06, B03, B09) invited Jan Albrecht (B07),… more ›
Upcoming Events
Scalable methods for Gaussian process regression
Botond Tibor Szabo, Bocconi University, Italy 2.28.0.10810:15 - 11:45
Gaussian processes (GP) are frequently used in Bayesian nonparametrics as a prior distribution on infinite dimensional functional parameters. However,…
more ›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 ›
Workshop: Push Your Career outside Academia – Part I Job Interview (online)
Heidi Störr, Push Your Career - Career Consultancy Online09:00-12:30
Leaving academia and looking for a job in the industry is an important and sometimes difficult step. This fundamental career training enables you to…
more ›Latest Publications
Tiepner, A. and Ziebell, E. (2024). Parameter estimation in hyperbolic linear SPDEs from multiple measurements. arXiv:2407.13461
Ziebell, E. (2024). Non-parametric estimation for the stochastic wave equation. arXiv:2404.18823
V. Pfeifer, V. Muraveva, and Beta, C. (2024). Flagella and Cell Body Staining of Bacteria with Fluorescent Dyes in Cell Motility and Chemotaxis: Methods and Protocols, edited by Carsten Beta and Cristina Martinez-Torres (Springer, 2024), p.79-85.