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

09-26-2023

German Chapter of the InterPore

This year the meeting of the German Chapter of the InterPore was held at KU Eichstätt-Ingolstadt on September 13-14, 2023. The meeting was organized at the Mathematical Institute for Machine Learning and Data Science (MIDS) by the chair of Geomatics and Geomathematics.
Around 30 researchers, PhD students, and professors participated in the two-day meeting. In addition to the two invited talks (see below), a total of 20 presentations were given on a wide range of research areas in the field of porous media ranging from experiment to theory and from science to industrial application.
During the coffee breaks and the dinner there were opportunities for exchanging ideas and deeper discussions.

The two invited speakers and their presentations are listed below:

Carina Bringedal (Western Norway University of Applied Sciences): Analysis and simulations of evaporation-driven density instabilities in porous media
Sergey Oladyshkin (University of Stuttgart, Institute for Water and Environmental Systems Modeling): Physics-Aware Neural Networks for uncovering unknown processes and leveraging the significance of Homogeneous Chaos Theory for learning

MIDS seminar

Mathematical Institute for Machine Learning and Data Science

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