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 21st 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. DAEDALUS is a collaboration of Technische Universität Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin.
We welcome our new PhD students!
We welcome our new PhD students, who were selected in our application process.
Congratulations to Leon Sallandt and co-authors who win paper award at ICML.
We congratulate Leon Sallandt and co-authors. Their paper ‘Solving high-dimensional parabolic PDEs using the tensor train format’ was accepted at the ICML (International Conference on Machine Learning) and received the “Outstanding Paper Honorable Mention” award.
Welcome to our new associated PIs!
We have recently admitted three new colleagues as associated PIs. We welcome Dr Marcus Weber (Zuse Institute), Dr Jan Hermann (FU Berlin) and Dr Alfonso Caiazzo (WIAS) to our team!