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, Institute of Mathematics
Lydia Stolpmann, University of Potsdam, Institute of Mathematics
Congratulations to Jin W. Kim (project A02) who won the CSL PhD Thesis Award from the University of Illinois for his thesis entitled "Duality for…
The workshop on Modern Approaches in SPDEs & Data Assimilation took place from July 3rd to 8th in Sibiu, Romania. The event was organized by the…
Congratulations to Lisa Schwetlick who successfully defended her PhD thesis on "Data Assimilation for Neurocognitive Models of Eye Movement" on June…
First session (07.12.; 09:00 - 12:30)
In the first session, this workshop focuses on what mental health is, what widespread mental health issues…more ›
Second session (08.12.; 09:00 - 12:30)
The second session focuses on the concept of resilience: what exactly is it, how does it serve us and how can…
Third session (12.12.; 19:00 - 21:00)
In this meeting you reflect together about your progress/current situation in an informal setting.
Birzhan Ayanbayev, Ilja Klebanov, Han Cheng Lie and T J Sullivan (2021). Gamma-convergence of Onsager–Machlup functionals: II. convergence of Onsager–Machlup functionals: II. Infinite product measures on Banach spaces. Inverse Problems, Volume 38, Number 2, doi:10.1088/1361-6420/ac3f82.
M. Redmann, M.A. Freitag (2021). Optimization based model order reduction for stochastic systems. Appl. Math. Comput., 398.
H. C. Lie, M. Stahn, T.J. Sullivan (2022). Randomised one-step time integration methods for deterministic operator differential equations. Calcolo, Volume 59, Number 13 doi:10.1007/s10092-022-00457-6.