  Classical physical modelling with associated numerical simulation
(model-based), and prognostic methods based on the analysis of large amounts of
data (data-driven) are the two most common methods used for the mapping of
complex physical processes. In recent years, the efficient combination of these
approaches has become increasingly important. Continuum mechanics in the core
consists of conservation equations that -- in addition to the always necessary
specification of the process conditions -- can be supplemented by
phenomenological material models. The latter are an idealized image of the
specific material behavior that can be determined experimentally, empirically,
and based on a wealth of expert knowledge. The more complex the material, the
more difficult the calibration is. This situation forms the starting point for
this work's hybrid data-driven and model-based approach for mapping a complex
physical process in continuum mechanics. Specifically, we use data generated
from a classical physical model by the MESHFREE software to train a Principal
Component Analysis-based neural network (PCA-NN) for the task of parameter
identification of the material model parameters. The obtained results highlight
the potential of deep-learning-based hybrid models for determining parameters,
which are the key to characterizing materials occurring naturally, and their
use in industrial applications (e.g. the interaction of vehicles with sand).
