A Novel Topology Optimization Approach using Conditional Deep Learning
This work addresses computational bottlenecks in engineering design optimization, but it is incremental as it adapts existing deep learning techniques to a specific domain.
The paper tackles the computational expense of conventional topology optimization by developing a conditional Wasserstein generative adversarial network (CWGAN) approach that replicates these algorithms at much lower cost, demonstrating a proof of concept with validation against traditional methods.
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.