A deep convolutional neural network for topology optimization with perceptible generalization ability

This article proposes a deep convolutional neural network with perceptible generalization ability for structural topology optimization. The architecture of the neural network is made up of encoding and decoding parts, which provide down- and up-sampling operations. The popular U-Net was adopted to improve the performance of the proposed neural network. To train the neural network, a large dataset is generated by Simplified Isotropic Material with Penalization (SIMP). The performance of the proposed method was evaluated by comparing its efficiency and accuracy with SIMP on a series of typical optimization problems. Results show that a significant reduction in computation cost was achieved with little sacrifice to the performance of design solutions. Furthermore, the generalization ability of the proposed method is discussed. This ability enables the model to obtain a solution to a problem when a boundary condition is not included in the training dataset with a certain accuracy.

Automated summary

This article introduces a deep convolutional neural network for structural topology optimization with perceptible generalization ability. It significantly reduces computation cost while maintaining accuracy in design solutions. Furthermore, the model can achieve solutions with a certain accuracy even when boundary conditions are not included in the training dataset.