论文标题
数据驱动的拓扑设计使用深层生成模型
Data-driven topology design using a deep generative model
论文作者
论文摘要
在本文中，我们提出了一种称为数据驱动拓扑设计的无灵敏度和多目标结构设计方法。它始终是从给定设计域中最初给定的材料分布的高性能材料分布。它的基本思想是迭代以下过程：（i）根据精明性从材料分布数据集中选择材料分布，（ii）使用与所选精英材料分布进行培训的深层生成模型生成新的材料分布，以及（iii）将生成的材料分布合并与数据集。由于深层生成模型的性质，生成的材料分布是多种多样的，并且继承了培训数据的特征，即精英材料分布。因此，预计某些生成的材料分布优于当前的精英材料分布，并且通过将生成的材料分布与数据集合并，可以改善新选择的精英材料分布的性能。通过迭代上述过程，进一步改善了性能。数据驱动拓扑设计的有用性通过数值示例证明。
In this paper, we propose a sensitivity-free and multi-objective structural design methodology called data-driven topology design. It is schemed to obtain high-performance material distributions from initially given material distributions in a given design domain. Its basic idea is to iterate the following processes: (i) selecting material distributions from a dataset of material distributions according to eliteness, (ii) generating new material distributions using a deep generative model trained with the selected elite material distributions, and (iii) merging the generated material distributions with the dataset. Because of the nature of a deep generative model, the generated material distributions are diverse and inherit features of the training data, that is, the elite material distributions. Therefore, it is expected that some of the generated material distributions are superior to the current elite material distributions, and by merging the generated material distributions with the dataset, the performances of the newly selected elite material distributions are improved. The performances are further improved by iterating the above processes. The usefulness of data-driven topology design is demonstrated through numerical examples.
