Events Calender

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.

SWIM Topic Meetings are one-day meetings bringing together researchers interested in one specific topic. To kick this off, we are hosting a meeting on Mathematical Modelling of Antimicrobial Resistance at University Medical Center Freiburg.
The target audience are researchers of all career stages working on AMR modelling (or planning to do so soon).

The HKMetrics network is a joint initiative of econometricians and statisticians from Heidelberg University, the Karlsruhe Institute of Technology and the University of Mannheim. The mission of HKMetrics is to promote and foster research in applied and theoretical econometrics to initiate joint research cooperations.

KIT Center MathSEE is one of the nine interdisciplinary research centers at Karlsruhe Institute of Technology. It pools knowledge between the sciences, engineering and economics and builds a bridge to the mathematical sciences achieving success through interdisciplinary research on mathematical methods for scientific and societal challenges. It also aims to bring experiences on a national, european and international scale together and be the town square for exchange of ideas, best practices and co-operations.
Through the symposium on applications of mathematics, the center fosters discussion and promotes new partnerships in science and with the industry. On-site costs are kindly covered by MathSEE so that the registration fee for the symposium itself is waived for all participants.
In September 2026, the third MathSEE Symposium will take place and in this spirit, we welcome you most warmly to join us at the symposium and become part of the Math-SEE community! 
The symposium will feature 8 plenary lectures and several topically focussed parallel sessions on the applications of mathematical methods along the following tracks:
Mathematical structures: Shapes, Geometry, Number Theory and Algebra Mathematical Modeling, Differential Equations, Numerics and Simulation Inverse problems and Optimization Stochastic Modeling, Statistical Data Analysis and Forecasting We are excited about your contribution and look forward to discuss, to share and to network to widen the mathematical community!