Deep learning for determining a near-optimal topological design without any iteration
This work addresses the computational bottleneck in structural design optimization, offering a faster alternative to traditional iterative methods, though it appears incremental as it builds on existing deep learning techniques.
The authors tackled the problem of topology optimization by proposing a deep learning method that predicts near-optimal structures directly from boundary conditions without iterative processes, achieving results with negligible computational time and high compliance.
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.