This project aims to develop and operationalize new prediction products for the integration of photovoltaics (PV) into the energy market and smart grids by delivering simulations of PV power output at high resolution.
HLRS is developing approaches for combining freely available data and supercomputing resources to create a new generation of searchable data products for European citizens, public authorities, economic operators, and decision makers.
This project coordinates support for HPC users in Baden-Württemberg and the implementation of related measures and activities, including data intensive computing and large-scale scientific data management.
This project is testing a novel, simulation-based approach to develop new systems for protecting vehicle occupants in accidents.
Eurolab4HPC2 worked to promote the consolidation of European research excellence in exascale HPC systems.
bwVisu provided powerful visualization resources to scientific institutions in Baden-Württemberg, including working toward the development of a scalable service for remote visualization of scientific data.
EUXDAT is a Horizon 2020 project building an e-infrastructure addressing agriculture, land monitoring, and energy efficiency for sustainable development.
EOPEN aims to tackle the technical barriers arising from the massive streams of Earth observation data to ensure scalability of the data harmonization, standardization, fusion, and exchange methods.
HPC Europa 3 fosters transnational cooperation among EU scientists (especially junior researchers) who work on HPC-related topics such as applications, tools, and middleware.
This federally funded project is researching possibilities of efficient data management with regard to high amounts of scientific data emerging from the programs of engineering science at the University of Stuttgart.
The Partnership for Advanced Computing in Europe supports high-impact scientific discovery and engineering R&D to enhance European competitiveness for the benefit of society.
The objective of this European training network for mechanical and computer science engineers is to develop advanced tools for analyzing fluid dynamics in large-scale models of turbine components and to eventually enable the virtual testing of an entire machine.
