[论文解读] A Novel Topology Optimization Approach using Conditional Deep Learning

Sharad Rawat, M.-H. Herman Shen|arXiv (Cornell University)|Jan 14, 2019

Topology Optimization in Engineering参考文献 17被引用 50

一句话总结

本文提出了一个条件Wasserstein GAN（CWGAN）框架，用于模拟平面结构的传统拓扑优化，在设定的设计条件下实现超低成本的优化设计生成。

ABSTRACT

In this study, a novel topology optimization approach based on conditional Wasserstein generative adversarial networks (CWGAN) is developed to replicate the conventional topology optimization algorithms in an extremely computationally inexpensive way. CWGAN consists of a generator and a discriminator, both of which are deep convolutional neural networks (CNN). The limited samples of data, quasi-optimal planar structures, needed for training purposes are generated using the conventional topology optimization algorithms. With CWGANs, the topology optimization conditions can be set to a required value before generating samples. CWGAN truncates the global design space by introducing an equality constraint by the designer. The results are validated by generating an optimized planar structure using the conventional algorithms with the same settings. A proof of concept is presented which is known to be the first such illustration of fusion of CWGANs and topology optimization.

研究动机与目标

• 通过借鉴传统算法来降低拓扑优化的计算成本。
• 开发基于CWGAN的模型，在带条件的设计参数下重现准最佳拓扑结构。
• 证明对设计进行条件化可截断设计空间并实现预先指定的优化标准。

提出的方法

• 使用带有深度CNN生成器和判别器的条件Wasserstein GAN。
• 在由传统拓扑优化算法生成的准最优平面结构上进行训练。
• 加入一个等式约束，将CWGAN条件化为设计师指定的要求。
• 通过使用与传统方法相同的设置生成一个优化的平面结构来进行验证。

实验结果

研究问题

• RQ1 CWGAN能否在平面结构上重现准最佳拓扑优化结果？
• RQ2 将CWGAN对设计要求进行条件化是否能有效约束设计空间？
• RQ3 在相同设置下，CWGAN生成的设计与传统拓扑优化输出有何差异？

主要发现

• 展示了将CWGAN与拓扑优化融合的概念验证。
• CWGAN可以生成对预定义设计数值进行条件化的拓扑样本。
• 该方法旨在以计算成本低的方式复现实验结果与传统优化结果。