  Machine learning has found its way into almost every area of science and
engineering, and we are only at the beginning of its exploration across fields.
Being a popular, versatile and powerful framework, machine learning has proven
most useful where classical techniques are computationally inefficient, which
applies particularly to computational solid mechanics. Here, we dare to give a
non-exhaustive overview of potential avenues for machine learning in the
numerical modeling of solids and structures and offer our (subjective)
perspective on what is yet to come.
