Deep learning for determining a near-optimal topological design without any iteration

TL;DR

This paper introduces a deep learning approach combining CNN and cGAN to predict near-optimal topological structures efficiently without iterative optimization, using datasets generated from traditional methods.

Contribution

It presents a novel non-iterative deep learning framework for topology optimization, integrating CNN and cGAN trained on multi-resolution datasets.

Findings

Accurately predicts optimized structures with minimal computational time.

Effective at both low and high resolution datasets.

Outperforms traditional iterative methods in speed and comparable in quality.

Abstract

In this study, we propose a novel deep learning-based method to predict an optimized structure for a given boundary condition and optimization setting without using any iterative scheme. For this purpose, first, using open-source topology optimization code, datasets of the optimized structures paired with the corresponding information on boundary conditions and optimization settings are generated at low (32 x 32) and high (128 x 128) resolutions. To construct the artificial neural network for the proposed method, a convolutional neural network (CNN)-based encoder and decoder network is trained using the training dataset generated at low resolution. Then, as a two-stage refinement, the conditional generative adversarial network (cGAN) is trained with the optimized structures paired at both low and high resolutions, and is connected to the trained CNN-based encoder and decoder network. The performance evaluation results of the integrated network demonstrate that the proposed method can determine a near-optimal structure in terms of pixel values and compliance with negligible computational time.