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Teilnahme an dem GK Daedalus mit der TU Berlin
Finanzierung:
Deutsche Forschungsgemeinschaft (DFG) ;
The main goal of DAEDALUS is the analysis of the interplay between incorporation of data and differential equation-based modeling, which is one of the key problems in model-based research of the 21th century. DAEDALUS focuses both on theoretical insights and on applications in life sciences (brain-computer interfaces and biochemistry) as well as in fluid dynamics. The projects cover a scientific range from machine learning, mathematical theory of model reduction and uncertainty quantification to respective applications in turbulence theory, simulation of complex nonlinear flows as well as of molecular dynamics in chemical and biological systems. In our group, we cover mathematical statistics and machine learning aspects.

This project is in the context of Daedalus, and is concerned with uncertainty quantification in complex cases.
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