Research within JARA CSD integrates novel methods in simulation and data sciences, including: innovative methods for the solution of nonlinear partial differential equations, continuous and discrete optimization, machine learning, physics-inspired data science, and high-performance computing. Our projects are application-oriented with a focus on societal challenges like energy crisis, personalized medicine, environment and global climate change.
ERS Prep Fund Projects in JARA CSD
Towards an integrated data science of complex natural systems
Involved JARA CSD members: Martin Grohe, Moritz Helias, Abigail Morrison, Holger Rauhut, Michael Schaub
Abstract: Quantitative natural sciences have a long, successful history of obtaining insights into nature by applying a reductionist, model-driven approach to explain empirical observations from a small set of principles. Identifying these principles is the very objective of science, and our ability to comprehend and ultimately control our world critically hinges on this deep understanding of nature.
Deep Image Data Analysis for Precision Medical Imaging
Involved JARA CSD member: Volkmar Schulz
Abstract: Modern medical imaging devices generate an ever-increasing data volume of surging information
granularity. In order to cope with this high data volume, the acquired raw data are currently heavily
filtered and compressed using classical, historically developed signal processing techniques.
High Performance Computing in the Geosciences
Involved JARA CSD members: Harrie-Jan Hendricks-Franssen, Harry Vereecken, Florian Wellmann
Abstract: Geoscientific studies have a major global economic and societal impact: They are fundamental
to understanding the physical, chemical, biogeochemical and biological processes of the Earth,
in order to develop forecasts, and derive strategies for action – and this aspect is also evident in
the fact that modeling of Earth and Environment is identified as one of the “Grand Challenge
Problems” in the House of Simulation and Data Science of JARA-CSD. Although geoscientific
investigations are extremely diverse, they share cross-sectional methodologies, as uncertainty
quantification and data assimilation. Due to the computationally demanding nature of these crosssectional
methodologies, High-Performance Computing (HPC) is of significant importance.