Neural networks for topology optimization
Neural networks for topology optimization
复制标题
DOI：
10.1515/rnam-2019-0018
复制
发表时间：
2019-08-01
影响因子：
0.6
通讯作者：
Oseledets, Ivan
中科院分区：
文献类型：
--
作者：
Sosnovik, Ivan;Oseledets, Ivan
在这项研究中，我们提出了一种基于深度学习的方法来加速拓扑优化方法。我们要解决的问题是布局问题。这项工作的主要新奇是国家的问题作为一个图像分割任务。我们利用深度学习方法的强大功能作为有效的逐像素图像标记技术来执行拓扑优化。我们介绍卷积编码器-解码器架构和以高性能解决上述问题的总体方法。所进行的实验表明优化过程的显着加速。该方法具有良好的泛化性能。我们证明了所提出的模型的应用到其他问题的能力。成功的结果，以及目前的方法的缺点，进行了讨论。
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.