A two-stage network framework for topology optimization incorporating deep learning and physical information

The advent of deep learning provides a promising opportunity to improve the efficiency of topology optimization. However, existing methods make it difficult to achieve a balance between efficiency, accuracy, and generalization ability. To tackle this challenge, we propose a novel method based on a two-stage network framework. In the network, the partial convolution block and shifted windows attention mechanism are integrated to improve the model performance. In the first stage, a convolutional neural network-based model trained with a novel-designed loss function is employed to achieve real-time prediction of suboptimal structures. In the second stage, transfer learning is introduced to inherit the output of the first stage. Subsequently, the second stage optimizes the suboptimal structures to get the final optimal structures in a physical information-driven way. On the 2000 dataset, the two-stage method achieves an average compliance error of −1.45%, and 95.5% of the optimal structures perform better than that obtained by the traditional method and strictly meet volume constraints while eliminating structural disconnections. Finally, the proposed method is applied to a real-world engineering application for the first time, and the design of bridge pylons is given as an example. The results show that the proposed method is a promising exploration of topology optimization based on deep learning.