  A key task in the field of modeling and analyzing nonlinear dynamical systems
is the recovery of unknown governing equations from measurement data only.
There is a wide range of application areas for this important instance of
system identification, ranging from industrial engineering and acoustic signal
processing to stock market models. In order to find appropriate representations
of underlying dynamical systems, various data-driven methods have been proposed
by different communities. However, if the given data sets are high-dimensional,
then these methods typically suffer from the curse of dimensionality. To
significantly reduce the computational costs and storage consumption, we
propose the method MANDy which combines data-driven methods with tensor network
decompositions. The efficiency of the introduced approach will be illustrated
with the aid of several high-dimensional nonlinear dynamical systems.
