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.

翻译：深度学习的最新进展表明其在拓扑优化领域具有巨大潜力。然而，尽管已有一些有前景的尝试，该子领域在基础方法和数据集方面仍缺乏坚实基础。本文旨在解决这两个问题。首先，我们探索基于物理的预处理和等变网络，以构建面向拓扑优化深度学习管道的样本高效组件。我们通过大规模消融研究，采用端到端监督训练对其进行评估。结果表明，样本效率与预测结果的物理正确性均获得显著提升。其次，为促进可比性与未来进展，我们发布了首批包含问题及其对应真实解的两个拓扑优化数据集。