Speaker
Christian Offen
Description
I will show how techniques in Geometric Numerical Integration can be exploited for data-driven system identification. I will demonstrate that exploiting Hamiltonian or variational structure can lead to increased accuracy in system identification by machine learning. Moreover, an exploit of data-driven symmetries can improve the extrapolation performance of machine-learned models and enables to detect conservation laws.
