Real-time topology optimization based on multi-scale convolutional attention mechanism

IF 2.2 3区工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY

Engineering Optimization Pub Date : 2024-07-09 DOI:10.1080/0305215x.2024.2357153

Wei Zhang, Lijie Su, Xianpeng Wang

引用次数: 0

Abstract

This article proposes a deep learning model based on a multi-scale convolutional attention mechanism network SegNext for improving Topology Optimization (TO-Next), aimed at addressing the computati...

查看原文本刊更多论文

基于多尺度卷积注意力机制的实时拓扑优化

本文提出了一种基于多尺度卷积注意力机制网络 SegNext 的深度学习模型，用于改进拓扑优化（TO-Next），旨在解决计算能力不足的问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Engineering Optimization 管理科学-工程：综合

CiteScore

5.90

自引率

7.40%

发文量

审稿时长

3.5 months

期刊介绍： Engineering Optimization is an interdisciplinary engineering journal which serves the large technical community concerned with quantitative computational methods of optimization, and their application to engineering planning, design, manufacture and operational processes. The policy of the journal treats optimization as any formalized numerical process for improvement. Algorithms for numerical optimization are therefore mainstream for the journal, but equally welcome are papers which use the methods of operations research, decision support, statistical decision theory, systems theory, logical inference, knowledge-based systems, artificial intelligence, information theory and processing, and all methods which can be used in the quantitative modelling of the decision-making process. Innovation in optimization is an essential attribute of all papers but engineering applicability is equally vital. Engineering Optimization aims to cover all disciplines within the engineering community though its main focus is in the areas of environmental, civil, mechanical, aerospace and manufacturing engineering. Papers on both research aspects and practical industrial implementations are welcomed.