  Recent developments in Deep Learning (DL) suggest a vast potential for
Topology Optimization (TO). However, while there are some promising attempts,
the subfield still lacks a firm footing regarding basic methods and datasets.
We aim to address both points. First, we explore physics-based preprocessing
and equivariant networks to create sample-efficient components for TO DL
pipelines. We evaluate them in a large-scale ablation study using end-to-end
supervised training. The results demonstrate a drastic improvement in sample
efficiency and the predictions' physical correctness. Second, to improve
comparability and future progress, we publish the two first TO datasets
containing problems and corresponding ground truth solutions.
