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Welcome to the page of the Professorship for Data Assimilation

In order to be able to predict severe weather events or the melting of ice in the Arctic, information in the form of heterogeneous data must be linked with numerical models of dynamic systems. This is done through data assimilation, which makes it possible to better investigate processes and predict their further development.  In the field of data assimilation, the professorship is concerned with the further development of data science algorithms by incorporating physical conservation laws, and solving correspondingly large optimisation problems in the environmental sciences. Quantifying the uncertainties of predictions, numerical models and observations also plays a central role here.

About us

Prof. Janjic introduces herself and the chair

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Math News

Approximation experts as guests at MIDS

We are very pleased to welcome Prof. Joaquín Jódar Reyes (Departamento Matemáticas Universidad de Jaen, Campus Las Lagunillas, Spain) Prof. Miguel L. Rodríguez (Dep. de Matemática Aplicada, ETSI de Caminos Canales y Puertos, Universidad de Granada, Spain) and Prof. Martin Buhmann (Justus-Liebig University Giessen) as guests at MIDS this week.

Together with Dr. Janin Jäger from Prof. Oliver's research group, they are working on improving approximation methods. These are methods for calculating approximations and functions from data. In particular, they are looking at quasi-interpolation methods, in which they do not attempt to transfer the measured values exactly into the approximation, as the values can be incorrect, but rather to smooth the data.

Prof. Buhmann also gave a talk on "New multiquadric-type interpolation and on a regular domain" at the MIDS Oberseminar on Tuesday.

In 2022, CUP published a book by Prof. Buhmann and Dr. Jäger with the title "Quasi-Interpolation"

We are always very happy to welcome international scientists at MIDS.

Mathematical Institute for Machine Learning and Data Science

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The Heisenberg professorship is part of the Mathematical Institute for Machine Learning and Data Science, MIDS.
Learn more about MIDS here.