A Novel Topology Optimization Approach using Conditional Deep Learning

TL;DR

This paper introduces a new topology optimization method using conditional Wasserstein GANs, enabling rapid generation of optimized structures based on conventional algorithms, significantly reducing computational costs.

Contribution

It presents the first integration of CWGANs with topology optimization, allowing condition-based, efficient structure generation constrained by designer specifications.

Findings

CWGAN can replicate conventional topology optimization results.

The method significantly reduces computational time.

Validated with planar structure generation.

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.