Our latest news
Data-driven use cases towards Exascale
One of the primary goals in CoE RAISE is the development and expansion of AI methods along representative use cases from research and industry, which have a strong focus on data-driven technologies, i.e., analyzing data-rich descriptions of physical phenomena.
On the way to lower energy consumption in the transport sector with the help of machine learning
The research in WP3 and the collaboration with WP2 is strongly related to the growing environmental awareness. To be more precise, it is a must in the transport sector to lower energy consumption which is strongly determined by friction drag.
The virtual Summer school of the IEEE GRSS HDCRS working group on High-Performance and Disruptive Computing in Remote Sensing
In collaboration with the University of Iceland (UOI) and Forschungszentrum Jülich (FZJ), CoE-RAISE co-organized the virtual event in the capacity of partner from May 31 to June 3, 2021.
Application Co-Design for an AI Framework for Exascale
One of the CoE RAISE goals is to design, implement, and evaluate an AI framework that is ready for future Exascale HPC systems (see ‘AI at Exascale‘ ). This framework is an enabler for highly scalable applications accelerating scientific discovery and advancing engineering in a wide variety of domains. It is co-designed by the RAISE Use Cases from natural sciences and engineering.
CoE RAISE oganized a seminar on its Interaction Room technique to boost interdisciplinary collaboration
CoE RAISE organized a seminar titled “HPC Systems Engineering in the Interaction Room” from 12:00-14:00 CEST on April 8th, 2021.