  Machine learning surrogate emulators are needed in engineering design and
optimization tasks to rapidly emulate computationally expensive physics-based
models. In micromechanics problems the local full-field response variables are
desired at microstructural length scales. While there has been a great deal of
work on establishing architectures for these tasks there has been relatively
little work on establishing microstructural experimental design strategies.
This work demonstrates that intelligent selection of microstructural volume
elements for subsequent physics simulations enables the establishment of more
accurate surrogate models. There exist two key challenges towards establishing
a suitable framework: (1) microstructural feature quantification and (2)
establishment of a criteria which encourages construction of a diverse training
data set. Three feature extraction strategies are used as well as three design
criteria. A novel contrastive feature extraction approach is established for
automated self-supervised extraction of microstructural summary statistics.
Results indicate that for the problem considered up to a 8\% improvement in
surrogate performance may be achieved using the proposed design and training
strategy. Trends indicate this approach may be even more beneficial when scaled
towards larger problems. These results demonstrate that the selection of an
efficient experimental design is an important consideration when establishing
machine learning based surrogate models.
