.png){kind=icon}
返回
Towards machine learned generative design
.png){kind=icon}
OA
AI
DOI：10.1016/j.autcon.2024.105284.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine learned generative design is an extension of the generative design process, addressing its inherent limitations, particularly those of interoperability. The proposed approach uses machine learning -based surrogate models, trained on computational model data, to replicate design evaluations and integrate them into a common environment. In this way, design alternatives can be generated and tested that satisfy all design requirements and considerations. The effectiveness of this approach is demonstrated by the design and optimisation of the enclosure structure for the New Robotic Telescope. Its complexity is characterised by multiple operating states that the enclosure can assume, in particular the closed state and the opening/closing state, each of which has a different structural behaviour. Using our approach, the results from each state were replicated with machine learning models and combined into a single evaluation model. This resulted in finding multiple solutions that outperformed the benchmark design. The results demonstrate not only the success of our method over conventional strategies, but also highlight its potential to redefine future design optimisation processes.
Keyword:
Computational design
Generative design
Machine learning
Optimisation
Surrogate modelling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11.5
论文数:
6.3K
被引数:
4.2W
机构
引用论文
Generative Design Methodology and Framework Exploiting Designer-Algorithm Synergies利用设计器-算法协同的生成设计方法和框架.png)
BUILDINGS
OAAI
BUILDINGSIF3.1
OAAI分享收藏Understanding effects of BIM on collaborative design and construction: An empirical study in China理解BIM对协同设计和施工的影响: 中国的实证研究OAAI
OAAI分享收藏A machine learning-based surrogate model for optimization of truss structures with geometrically nonlinear behavior基于机器学习的代理模型，用于优化具有几何非线性行为的桁架结构
PREAI
分享收藏
查看更多内容