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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

Interpore 2026

In May, the 18th Interpore Conference took place in Nantes, France. Prof. Dr. Nadja Ray (Chair of Geomatics and Geomathematics at MIDS) attended on behalf of KU. In addition to participating in the conference, Prof. Ray also organized a mini-symposium and two poster presentations. The steering committee of the German chapter also met, and there were many opportunities for professional exchange. 

(MS07) Mathematical and numerical methods for multi-scale multi-physics, nonlinear coupled processes

Lead Organizer: Nadja Ray - Katholische Universität Eichstätt-Ingolstadt (KU), Germany

  • Tuanny Cajuhi - Federal Institute for Geosciences and Natural Resources (BGR), Germany
    • Mostafa Mollaali - Helmholtz Center for Environmental Research (UFZ), Germany
    • Keita Yoshioka -  Technical University of Leoben, Austria
    • Advanced mathematical models and analyses of nonlinear coupled equations, sharp interface as well as phase field formulations
    • Multiscale and model order reduction techniques (e.g., homogenization, upscaling)
    • Novel discretization schemes and solvers to improve accuracy and computational efficiency
    • Robust nonlinear and linear solution strategies
    • Integration of experimental data and application of AI and machine learning tools
    • Marcio Murad - National Laboratory for Scientific Computation (LNCC), Brazil
      Discontinuous Galerkin Method for Flow in Enlarged Fractured Carbonates
    • Son-Young Yi - The University of Texas at El Paso, USA
      Physics-preserving enriched Galerkin method for a fully-coupled thermo-poroelasticity model

 Processes in porous media — including flow, transport, deformation, and reactions — are central to applications in energy, environment, and advanced materials. Experiments and field observations are often costly or infeasible, making mathematical modeling and simulation essential.

Challenges arise from strong heterogeneity, multiscale and multiphysics interactions, or phase transitions. This minisymposium invites contributions on:

The minisymposium aims to foster exchange on methodologies and applications for porous media in the geosciences at both laboratory and field scales, as well as on bridging across scales.

Solicited Speaker:

  • Marcio Murad - National Laboratory for Scientific Computation (LNCC), Brazil
    Discontinuous Galerkin Method for Flow in Enlarged Fractured Carbonates
    • Son-Young Yi - The University of Texas at El Paso, USA
      Physics-preserving enriched Galerkin method for a fully-coupled thermo-poroelasticity model

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.