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Deep concept identification for generative design

delete2025-05-01
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OA
AI
R
Ryo Tsumoto
K
Kentaro Yaji
Y
Yutaka Nomaguchi
DOI：10.1016/j.aei.2025.103354delete
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摘要

摘要

En 中文
生成式设计技术已发展成为通过结合拓扑优化与人工智能技术来生成多样化替代方案的高级方法。随着其多样性增加，设计师在选择最合适替代方案时所承受的认知负担也随之增加。概念识别方法（用于发现实体的各种类别）有望有效系统地解释其多样性。然而，传统的概念识别方法在几何属性面临高维度时无法提供有意义的类别。为解决这一挑战，本研究提出了一种基于深度学习（DL）技术的生成式设计新概念识别框架。深度学习的一项关键能力是自动学习特定任务的有效表示。本研究首先概述了基于通用设计理论的概念识别要点，然后提出一个基本框架，包括使用生成式设计技术生成多样化替代方案、使用DL技术将替代方案聚类为若干类别，以及使用分类模型将这些类别整理为设计概念。本研究通过实现基于DL范式的生成和聚类模型——变分深度嵌入，以及作为分类模型的逻辑回归，展示了其基本能力。其实现应用于二维桥梁结构的简化设计问题作为案例研究。所提出的深度概念识别框架能够系统地识别多样化替代方案的有意义类别，但由于数据驱动方法与概念识别本质之间存在差距，该框架在若干步骤中仍需依赖设计师的认知。
Keyword:
Concept identification
Design concept
Generative design
Topology optimization
Representation learning
Clustering
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期刊

Advanced Engineering Informatics 封面图
Advanced Engineering Informatics
IF:
9.9
论文数:
4.5K
被引数:
1.7W

机构

O
Osaka Univ
学者数:
2.0K
论文数: 890
被引数: 241
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