  In this work, we propose a fully coupled multiscale strategy for components
made from short fiber reinforced composites, where each Gauss point of the
macroscopic finite element model is equipped with a deep material network (DMN)
which covers the different fiber orientation states varying within the
component. These DMNs need to be identified by linear elastic precomputations
on representative volume elements, and serve as high-fidelity surrogates for
full-field simulations on microstructures with inelastic constituents. We
discuss how to extend direct DMNs to account for varying fiber orientation, and
propose a simplified sampling strategy which significantly speeds up the
training process. To enable concurrent multiscale simulations, evaluating the
DMNs efficiently is crucial. We discuss dedicated techniques for exploiting
sparsity and high-performance linear algebra modules, and demonstrate the power
of the proposed approach on an industrial-scale three-dimensional component.
Indeed, the DMN is capable of accelerating two-scale simulations significantly,
providing possible speed-ups of several magnitudes.
