In topology optimization using deep learning, load and boundary conditions represented as vectors or sparse matrices often miss the opportunity to encode a rich view of the design problem, leading to less than ideal generalization results. We propose a new data-driven topology optimization model called TopologyGAN that takes advantage of various physical fields computed on the original, unoptimized material domain, as inputs to the generator of a conditional generative adversarial network (cGAN). Compared to a baseline cGAN, TopologyGAN achieves a nearly $3\times$ reduction in the mean squared error and a $2.5\times$ reduction in the mean absolute error on test problems involving previously unseen boundary conditions. Built on several existing network models, we also introduce a hybrid network called U-SE(Squeeze-and-Excitation)-ResNet for the generator that further increases the overall accuracy. We publicly share our full implementation and trained network.

中文翻译：

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TopologyGAN：使用基于初始域物理场的生成对抗网络进行拓扑优化

在使用深度学习的拓扑优化中，表示为向量或稀疏矩阵的负载和边界条件通常会错过对设计问题的丰富视图进行编码的机会，从而导致不太理想的泛化结果。我们提出了一种新的数据驱动拓扑优化模型，称为 TopologyGAN，该模型利用在原始未优化材料域上计算的各种物理场，作为条件生成对抗网络 (cGAN) 生成器的输入。与基线 cGAN 相比，TopologyGAN 在涉及以前看不见的边界条件的测试问题上，均方误差减少了近 3 倍，平均绝对误差减少了 2.5 倍。建立在几个现有的网络模型之上，我们还为生成器引入了一个称为 U-SE(Squeeze-and-Excitation)-ResNet 的混合网络，进一步提高了整体精度。我们公开分享我们的完整实施和训练有素的网络。