Topic 1 Enabling Computational- & Data-Intensive Science and Engineering

To this end the activities at KIT include domain-specific research and consulting in research data management, data infrastructures, performance engineering, artificial intelligence (AI), machine learning and data analytics.

A prime objective of Topic 1 is to achieve profound competence in the management and analytics of large-scale, often heterogeneous data sets as well as the most complex (data-driven) models and theories through research & development (R&D) on and application of state-of-the-art research data management procedures, data analytics workflows, advanced machine learning and AI methods. Another core objective is to research and develop scalable advanced simulation algorithms and tools for the latest supercomputers approaching the exascale including disruptive technologies such as quantum computing. To realize these objectives, it is essential that R&D activities are interdisciplinary and that researchers from computer science and mathematics work closely together with different application domains (e.g., climate, materials, and energy sciences as well as physics), each having their own scientific challenges. A further central and binding objective involves research on cross-domain generic activities in computer science and mathematics (e.g., on big data methods, exascale algorithms, artificial intelligence, and uncertainty quantification). This ensures synergy and knowledge transfer of both generic and applied algorithms, methods, tools, and software between the different application areas aiming to use data-intensive computing systems more efficiently today and in the future.