论文标题
Topologygan：基于初始域上的物理领域的生成对抗网络的拓扑优化
TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
论文作者
论文摘要
在使用深度学习，负载和边界条件的拓扑优化中，表示为向量或稀疏矩阵通常会错过对设计问题的丰富视图的机会，从而导致不理想的概括结果。我们提出了一个称为TopologyGAN的新的数据驱动拓扑优化模型，该模型利用原始，未优化的材料域计算的各种物理领域，作为条件生成对抗网络（CGAN）的发电机的输入。与基线CGAN相比，Topologygan的平均平方误差降低了近3美元的$ 3 \ tims $ $降低，$ 2.5 \ tims $减少了涉及以前看不见的边界条件的测试问题的平均绝对误差。我们还基于几种现有的网络模型，为发电机引入了一个称为U-SE（挤压和兴奋）的混合网络，以进一步提高整体准确性。我们公开分享我们的完整实施和训练有素的网络。
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.
