title: ÐÐµÐ·ÑÑÐµÑÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ðµ ÑÐµÑÐµÐ½Ð¸Ðµ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐ¾Ð¿Ð¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¾Ð¹ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ ÑÐ¿ÑÑÐ³Ð¸Ñ ÑÐµÐ» Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Non-iterative topology optimization of elastic bodies using machine learning
Creators: name: ÐÐ°ÑÐ²ÐµÐµÐ²Ð°, ÐÐ½Ð°ÑÑÐ°ÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ¾Ð¿Ð¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ°Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ
Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½ÑÐµ Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»Ñ
Ð¼ÐµÑÐ¾Ð´ ÑÐºÐ¾Ð»ÑÐ·ÑÑÐ¸Ñ Ð°ÑÐ¸Ð¿ÑÐ¾Ñ
topological optimization
adaptive materials
moving asymptotes

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description: In this graduation thesis, as an alternative to traditional methods of topology optimization of deformable solids, an approach to topology optimization based on machine learning methods is proposed. The proposed approach significantly reduces the time spent on obtaining the optimal solution and allows to avoid the use of time-consuming finite element calculations at the stage of obtaining the optimal solution. All of time-consuming calculations are performed at the network training stage. A two-stage model, comprising a convolutional neural network (CNN) and a generative adversarial network (GAN), is used to predict the optimal topology. In the first stage, the CNN predicts the topology at a low resolution based on load information and volume fraction. Subsequently, the generated topology is refined and its resolution is increased using the GAN. The effectiveness of the proposed method has been successfully demonstrated on a square elastic plate. The results and unresolved issues of the method are also discussed.