We present a sample-efficient deep learning strategy for topology optimization. Our end-to-end approach is supervised and includes physics-based preprocessing and equivariant networks. We analyze how different components of our deep learning pipeline influence the number of required training samples via a large-scale comparison. The results demonstrate that including physical concepts not only drastically improves the sample efficiency but also the predictions' physical correctness. Finally, we publish two topology optimization datasets containing problems and corresponding ground truth solutions. We are confident that these datasets will improve comparability and future progress in the field.

翻译：我们提出了一种具有抽样效率的深层学习战略,以优化地形。我们的端对端方法受到监督,包括基于物理的预处理和等同网络。我们通过大规模比较分析我们深层学习管道的不同组成部分如何影响所需培训样本的数量。结果显示,包括物理概念不仅极大地提高了抽样效率,而且大大提高了预测的物理正确性。最后,我们公布了两个包含问题和相应的地面真相解决方案的顶层优化数据集。我们相信,这些数据集将改善实地的可比性和今后的进展。