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
Registration for the workshop on Statistical Inference for Stochastic PDEs co-organized by Randolf Altmeyer, Markus Reiß and Wilhelm Stannat, members...
Once a year the research institutes, including the University of Potsdam, present their work to the interested fellow citizens and give insights into...
Congratulations to our SFB speaker Sebastian Reich for having been selected into the SIAM Fellows Class of 2019.
The Society for Industrial and...
There is a certain magic involved in recasting the equations in Physics, and the algorithms in Engineering, in variational terms. The most classical...more ›
Coupled models simulate different compartments of the Earth system as well as their interactions. For example coupled ocean-biogoechemical models...more ›
The annual meeting of SFB1294 will take place on the 23rd of September at the Campus Griebnitzsee.
More information, incl. the agende, will follow...more ›
Avanesov, V. (2019). Nonparametric Change Point Detection in Regression. arXiv:1903.02603
Götze, F., Naumov, A., Spokoiny, V. and Ulyanov, V. (2019). Gaussian comparison and anti-concentration inequalities for norms of Gaussian random elements, Bernoulli, in print. arXiv:1708.08663
Zadorozhnyi, O., Blanchard, G. and Carpentier, A. (2019). Restless dependent bandits with fading memory. arXiv: 1906.10454