The range of relevant scales is so vast that we cannot expect that the exponentially growing capacity of modern supercomputers alone will suffice to make scientific progress. We also need to improve our algorithms, develop parametrizations - simplified models for small scale processes - and learn from data and observations. The research of the group includes structure preserving and energy consistent algorithms, mathematical approaches for model reduction, and dynamic, stochastic, and/or data driven models to represent processes at scales below the computational grid.
Prof. Oliver works on modeling and simulation of complex multi-scale systems, in particular in climate science, but also in the bio and material sciences. He is member of the Board of the Collaborative Research Center TRR 181 "Energy Transfers in Atmosphere and Ocean"
The Chair of Reliable Maschine Learning is part of the new founded Mathematical Institute for Machine Learning and Data Science, MIDS.
Learn more about MIDS here.