A Novel Topology Optimization Approach using Conditional Deep Learning

Understand

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.