A Novel Topology Optimization Approach using Conditional Deep Learning

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.

8 Pages, 6 Figures, 1 Table. arXiv admin note: text overlap with arXiv:1808.02334

References in corpus (2)

• Conditional Generative Adversarial Nets
• Improved Techniques for Training GANs

Cited by in corpus (7)

• Topology optimization of 2D structures with nonlinearities using deep learning
• Algorithmically-Consistent Deep Learning Frameworks for Structural Topology Optimization
• An AI-Assisted Design Method for Topology Optimization Without Pre-Optimized Training Data
• A study on using image based machine learning methods to develop the surrogate models of stamp forming simulations
• Improving Solar Cell Metallization Designs using Convolutional Neural Networks
• SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Metal Forming Stress Field with Attention Mechanism and Perceptual Features
• Shape related constraints aware generation of Mechanical Designs through Deep Convolutional GAN