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
Congratulations to our SFB speaker Sebastian Reich for having been selected into the SIAM Fellows Class of 2019.
The Society for Industrial and...
Our joint ‘Workshop on Conservation Principles, Data and Uncertainty in Atmosphere-Ocean Modelling’ of the TRR 181, SFB 1114 and SFB 1294 took place...
Our 2nd SpringSchool took place at the Ostseehotel Dierhagen from the 18.03. to the 22.03.2019, where 60 members and associated members of the SFB,...
Talk by Julia Fleischer and Markus Seyfried
Das Projekt Kabinettwatch beschäftigt sich mit der Vorhersage der Zusammensetzung des Bundeskabinetts...more ›
One of the main characteristics of infinite-dimensional dissipative evolution equations, such as the Navier-Stokes equations and reaction-diffusion...more ›
The training school is part of the 50th anniversary program initiated by the CRM (Centre de recherches mathématiques) and will take place at the...more ›
V. Avanesov (2019). Nonparametric Change Point Detection in Regression. arXiv:1903.02603
J. Katz-Samuels, G. Blanchard, C. Scott (2019). Decontamination of Mutual Contamination Models. To appear in Journal of Machine Learning Research. Arxiv 1710.01167
G. Blanchard, N. Mücke (2018). Parallelizing Spectral Algorithms for Kernel Learning. Journal of Machine Learning Research (30):1-29, 2018. Open Access