Projects (active)
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The main goal of the present project is the further development and validation of a new computational fluid dynamics (CFD) method using a combination of grid-free (particles) and grid-based techniques. A fundamental assumption of this novel approach is the decomposition of any physical quantity into the grid based (large scale) and the fine scale parts, whereas large scales are resolved on the grid and fine scales are represented by particles. Dynamics of large and fine scales is calculated from two coupled transport equations one of which is solved on the grid whereas the second one utilizes the Lagrangian grid free Vortex Particle Method (VPM).


Herz-Kreislauf-Erkrankungen zählen zu den weltweit häufigsten Todesursachen: Jedes Jahr sterben in Deutschland über 300 000 Menschen an den Folgen. Rund die Hälfte dieser Todesfälle werden durch Herzrhythmusstörungen verursacht. Im europäischen Projekt MICROCARD, an dem das Karlsruher Institut für Technologie (KIT) beteiligt ist, entwickeln Forschende nun eine Simulationsplattform, die die elektrophysikalischen Signalübertragungen im Herzen digital abbilden kann. Die Computersimulationen sollen insbesondere zu einer verbesserten Diagnose und Therapie beitragen. Das KIT erhält für seine Beiträge im Rahmen des „European High-Performance Computing Joint Undertaking“ etwa 1,3 Millionen Euro.


The Simulated Worlds project aims to provide students in Baden-Württemberg with a deeper critical understanding of the possibilities and limitations of computer simulations. The project is jointly supported by the Steinbuch Centre for Computing (SCC), the High Performance Computing Center Stuttgart (HLRS) and the University of Ulm and is already working with several schools in Baden-Württemberg.


The HiRSE concept sees the establishment of central activities in RSE and the targeted sustainable funding of strategically important codes by so-called Community Software Infrastructure (CSI) groups as mutually supportive aspects of a single entity.


In the Research Training Group Tailored Scale-Bridging Approaches to Computational Nanoscience we investigate problems, that are not tractable by computational chemistry standard tools. The research is organized in seven projects. Five projects address scientific challenges such as friction, materials aging, material design and biological function. In two further projects, new methods and tools in mathematics and computer science are developed and provided for the special requirements of these applications. The SCC is involved in projects P4. P5 and P6.


Despite steady improvements in numerical weather prediction models, they still exhibit systematic errors caused by simplified representations of physical processes, assumptions about linear behavior, and the challenges of integrating all available observational data. Weather services around the world now recognize that addressing these shortcomings through the use of artificial intelligence (AI) could revolutionize the discipline in the coming decades. This will require a fundamental shift in thinking that integrates meteorology much more closely with mathematics and computer science. TEEMLEAP will foster this cultural change through a collaboration of scientists from the KIT Climate and Environment and MathSEE centers by establishing an idealized testbed to explore machine learning in weather forecasting. In contrast to weather services, which naturally focus on improvements of numerical forecast models in their full complexity, TEEMLEAP intends to evaluate the application possibilities and benefits of AI in this testbed along the entire process chain of weather forecasting.


The primary objective of the project is to establish an integrated nationwide computing and data infrastructure and to increase efficiency and effectiveness by providing first-class support to scientists and users.


Forschungsdatenmanagement bildet die Grundlage, um beispielsweise moderne Methoden der künstlichen Intelligenz für Forschungsfragen anwenden zu können. Daher ist Forschungsdatenmanagement ein wichtiger Bestandteil des KIT-Zentrums Klima und Umwelt. Im Projekt SmaRD-AI (kurz für Smart Research Data Management to facilitate Artificial Intelligence in Climate and Environmental Sciences) arbeiten am KIT das IWG, IMK, GIK und SCC eng zusammen, um den am KIT vorhandenen Datenschatz an Klima- und Umweltdaten nicht nur zugänglich zu machen, sondern auch über Werkzeuge strukturiert analysieren zu können.