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Donnerstag, 20. November 2025
12:30 - 17:00 
Workshop: Statistics and Data Science for Climate and Weather Extremes
Workshop
Blücherstraße 17
KIT Campus South …

This workshop brings together experts and researchers to discuss the latest statistical and data science methods for understanding and predicting extreme climate and weather events, including talks by Sebastian Engelke, Linda Mhalla, Gabriele Messori and Petra Friederichs

17:00 - 18:00 
MathSEE Members Assembly
Blücherstraße 17
KIT Campus South …

All members of KIT center MathSEE meet once a year, to discuss ongoing projects, funding opportunities and follow up on progress of the KIT Center MathSEE. This years members assembly will take place on Nov 20th, 17 - 18h, following a workshop on statistics and data science for climate and weather also at the same venue. Elections of new steering committee members and awarding of KCDS certificates will round up the members assembly. All members are most cordially invited to join. 

Montag, 02. März 2026
 
Workshop – Data-driven methods for partial differential equations
TBD

The workshop aims to unite the theoretical rigor of numerical mathematics, the stochastic analysis of data inaccuracies, and the flexibility and adaptability of machine learning. This interdisciplinary convergence is still in its infancy, and the workshop would initiate a systematic interaction between these communities. By exploring how error estimation, adaptivity, and convergence analysis can inform and enhance machine learning models for PDEs, we hope to lay the foundation for a new generation of hybrid computational surrogate methods that are reliable, data-aware, and numerically robust and efficient. To achieve these goals, the workshop will have several experts in the field give plenary lectures, have a poster session to foster extensive exchange of ideas, and a panel discussion with additional speakers from industry. 

Montag, 23. März 2026
9:00 
9th Spring School | Lattice Boltzmann Methods
University of Liverpool (UOL), Rendall Building, Liverpool L69 7WW, United Kingdom

Objective The spring school introduces researchers and users from industry to the theory of LBM and trains them on practical problems. Option B: the first half of the week is dedicated to theoretical fundamentals up to ongoing research on selected topics in kinetic theory, scientific computing, LBM, and Partial Differential Equations (PDE). Followed by mentored training on case studies using OpenLB in the second half of the week. Emphasis is placed on the modelling and simulation of particulate, multi-component, and turbulent fluid flows. Option A: Advanced OpenLB users and developers are enabled to solve their own application problems and implement their own solution approaches.
This educational concept is unique in the LBM community and offers a comprehensive and personal guided approach to LBM. Participants also benefit from the knowledge exchange during the poster session, coffee breaks and an excursion.
 
Executive committee John Bridgeman (UOL), Davide Dapelo (UOL), Mohaddeseh Mousavi Nezhad (UOL), Shota Ito (LBRG/KIT), Mathias J. Krause (LBRG/KIT), Stephan Simonis (ETH Zürich)  
Host Organization University of Liverpool (UOL)
 
Venue University of Liverpool (UOL), Rendall Building, Liverpool L69 7WW, United Kingdom
 
The Field of Lattice Boltzmann Method Lattice Boltzmann Methods (LBM) are an established numerical technique for Computational Fluid Dynamics (CFD) and beyond. The simulation of complex multi-physics benefits strongly from the mesoscopic modelling of LBM and positions it next to traditional numerical methods. The rapid development in LBM – also driven by the emergence of massive parallel computing infrastructure – enables engineers to solve relevant problems for academia as well as for industry.
 
Target audience The expected attendees are developers and researchers, from industry and academia interested to learn theoretical and practical aspects of LBM. The spring school addresses e.g. engineers, computer scientists, mathematicians and physicists as well as Master and PhD students. The course level is either beginners (Option B) or advanced (Option A). Based on the interest in CFD, this course provides a collaborative platform for LBM, both for developers and researchers.