A deep Convolutional Neural Network for topology optimization with strong generalization ability

TL;DR

This paper introduces a deep CNN with U-Net architecture for structural topology optimization, achieving high accuracy and efficiency, and demonstrating strong generalization to unseen boundary conditions.

Contribution

The paper develops a CNN-based method with U-Net structure that significantly reduces computation time while maintaining solution quality and generalizes well to new boundary conditions.

Findings

Significant reduction in computation cost compared to traditional methods

Maintains high accuracy in optimal structure layout

Generalizes effectively to boundary conditions not in training data

Abstract

This paper proposes a deep Convolutional Neural Network(CNN) with strong 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. In addition, a popular technique, namely U-Net, was adopted to improve the performance of the proposed neural network. The input of the neural network is a well-designed tensor with each channel includes different information for the problem, and the output is the layout of the optimal structure. To train the neural network, a large dataset is generated by a conventional topology optimization approach, i.e. 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 on the optimality of design solutions. Furthermore, the proposed method can intelligently solve problems under boundary conditions not being included in the training dataset.