  Traditional design cycles for new materials and assemblies have two
fundamental drawbacks. The underlying physical relationships are often too
complex to be precisely calculated and described. Aside from that, many unknown
uncertainties, such as exact manufacturing parameters or materials composition,
dominate the real assembly behavior. Machine learning (ML) methods overcome
these fundamental limitations through data-driven learning. In addition, modern
approaches can specifically increase system knowledge. Representation Learning
allows the physical, and if necessary, even symbolic interpretation of the
learned solution. In this way, the most complex physical relationships can be
considered and quickly described. Furthermore, generative ML approaches can
synthesize possible morphologies of the materials based on defined conditions
to visualize the effects of uncertainties. This modern approach accelerates the
design process for new materials and enables the prediction and interpretation
of realistic materials behavior.
