Neural networks for topology optimization

TL;DR

This paper introduces a deep learning approach that formulates topology optimization as an image segmentation task, significantly accelerating the process while maintaining high performance and generalization capabilities.

Contribution

It presents a novel convolutional encoder-decoder architecture for topology optimization, framing it as an image segmentation problem, which is a new perspective in the field.

Findings

Significant acceleration of the topology optimization process.

High generalization ability of the proposed model.

Successful application to different problems.

Abstract

In this research, we propose a deep learning based approach for speeding up the topology optimization methods. The problem we seek to solve is the layout problem. The main novelty of this work is to state the problem as an image segmentation task. We leverage the power of deep learning methods as the efficient pixel-wise image labeling technique to perform the topology optimization. We introduce convolutional encoder-decoder architecture and the overall approach of solving the above-described problem with high performance. The conducted experiments demonstrate the significant acceleration of the optimization process. The proposed approach has excellent generalization properties. We demonstrate the ability of the application of the proposed model to other problems. The successful results, as well as the drawbacks of the current method, are discussed.