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生成对抗网络在建筑设计领域的应用研究综述

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随着计算机科学的发展,人工智能应用渗透各个领域.为探究生成对抗网络在建筑设计领域的应用,本文将现有的国内外相关研究成果按照不同主题,分为建筑设计与生成、设计优化与预测、建筑风格转换与设计灵感、数据增强与评估四大类,并在此基础上,分析生成对抗网络在建筑设计领域的发展现状与未来的研究趋势,提出其未来可能的应用方向与潜在挑战,为研究者提供新的思路和灵感,从而推动建筑设计领域的创新和发展.
A REVIEW OF THE APPLICATION OF GENERATIVE ADVERSARIAL NETWORKS IN THE FIELD OF ARCHITECTURAL DESIGN
As computer science advancing, applications of artificial intelligence have permeated various fields. To explore the application of Generative Adversarial Networks (GANs) in the field of sarchitectural design, according to different themes, this paper summarizes do-mestic and international related research achievements which are divided into four major categories: architectural design and generation, design optimization and predic-tion, architectural style conversion and design inspiration, and data enhancement and evaluation. On this basis, this paper analyzes the current development status and future research trends of GANs in the field of architectural design, and puts forward possible application directions and potential challenges for future research to provide new ideas and inspiration for researchers, and promote innovation and development in the field of architectural design.

generative adversarial networksarchitectural designdeep learningapplication research

宋明星、林云菲

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湖南大学建筑与规划学院

生成对抗网络 建筑设计 深度学习 应用研究

湖南省自然科学基金

2023JJ30148

2024

当代建筑

当代建筑

ISSN:
年,卷(期):2024.(3)
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