  One of the core concepts in science, and something that happens intuitively
in every-day dynamic systems modeling, is the combination of models or methods.
Especially in dynamical systems modeling, often two or more structures are
combined to obtain a more powerful or efficient architecture regarding a
specific application (area). Further, even physical simulations are combined
with machine learning architectures, to increase prediction accuracy or
optimize the computational performance. In this work, we shortly discuss, which
types of models are usually combined and propose a model interface that is
capable of expressing a width variety of mixed algebraic, discrete and
differential equation based models. Further, we examine different established,
as well as new ways of combining these models from a system theoretical point
of view and highlight two challenges - algebraic loops and local event affect
functions in discontinuous models - that require a special approach. Finally,
we propose a new wildcard topology, that is capable of describing the generic
connection between two combined models in an easy to interpret fashion that can
be learned as part of a gradient based optimization procedure. The
contributions of this paper are highlighted at a proof of concept: Different
connection topologies between two models are learned, interpreted and compared
applying the proposed methodology and software implementation.
