  Crash simulations play an essential role in improving vehicle safety, design
optimization, and injury risk estimation. Unfortunately, numerical solutions of
such problems using state-of-the-art high-fidelity models require significant
computational effort. Conventional data-driven surrogate modeling approaches
create low-dimensional embeddings for evolving the dynamics in order to
circumvent this computational effort. Most approaches directly operate on
high-resolution data obtained from numerical discretization, which is both
costly and complicated for mapping the flow of information over large spatial
distances. Furthermore, working with a fixed resolution prevents the adaptation
of surrogate models to environments with variable computing capacities,
different visualization resolutions, and different accuracy requirements. We
thus propose a multi-hierarchical framework for structurally creating a series
of surrogate models for a kart frame, which is a good proxy for
industrial-relevant crash simulations, at different levels of resolution. For
multiscale phenomena, macroscale features are captured on a coarse surrogate,
whereas microscale effects are resolved by finer ones. The learned behavior of
the individual surrogates is passed from coarse to finer levels through
transfer learning. In detail, we perform a mesh simplification on the kart
model to obtain multi-resolution representations of it. We then train a
graph-convolutional neural network-based surrogate that learns
parameter-dependent low-dimensional latent dynamics on the coarsest
representation. Subsequently, another, similarly structured surrogate is
trained on the residual of the first surrogate using a finer resolution. This
step can be repeated multiple times. By doing so, we construct multiple
surrogates for the same system with varying hardware requirements and
increasing accuracy.
