Design Concept Generation With Variational Deep Embedding Over Comprehensive Optimization

K. Fujita, K. Minowa, Yutaka Nomaguchi, S. Yamasaki, K. Yaji
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引用次数: 6

Abstract

This paper proposes a framework for generating design concepts through the loop of comprehensive exploitation and consequent exploration. The former is by any sophisticated optimization such as topology optimization with diversely different. The latter realization is due to the variational deep embedding (VaDE), a deep learning technique with classification capability. In the process of design concept generation first, exploitation through computational optimization generates various possibilities of design entities. Second, VaDE learns them. This learning encodes the clusters of similar entities over the latent space with smaller dimensions. The clustering result reveals some design concepts and identifies voids where as-yet-unrecognized design concepts are prospective. Third, the decoder of the learned VaDE generates some possibilities for new design entities. Forth such new entities are examined, and relevant new conditions will trigger further exploitation by the optimization. In this paper, this framework is implemented for and applied to the conceptual design problem of bridge structures. This application demonstrates that the framework can identify voids over the latent space and explore the possibility of new concepts. This paper brings up some discussion on the promises and possibilities of the proposed framework.
基于变分深度嵌入的综合优化设计概念生成
本文提出了一个通过综合开发和后续探索的循环生成设计概念的框架。前者是由任何复杂的优化如拓扑优化具有不同的不同。后者的实现是由于变分深度嵌入(VaDE),一种具有分类能力的深度学习技术。在先产生设计概念的过程中,通过计算优化进行开发,产生设计实体的各种可能性。第二,VaDE学习它们。这种学习方法对具有较小维度的潜在空间上相似实体的簇进行编码。聚类结果揭示了一些设计概念,并确定了尚未被识别的设计概念的潜在空间。第三,学习后的VaDE的解码器为新的设计实体产生了一些可能性。第四,对这些新实体进行考察,并通过优化引发相关新条件的进一步开发。本文将该框架实现并应用于桥梁结构概念设计问题。该应用表明,该框架可以识别潜在空间上的空洞,并探索新概念的可能性。本文对拟议框架的承诺和可能性进行了一些讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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