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PDF papers
id acadia19_404
authors Liu, Henan; Liao, Longtai; Srivastava, Akshay
year 2019
source ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 404-411
summary Within the context of continuous technology transformations, the way scientists and designers process data is changing dramatically from simplification and explicit defined rules to searching and retrieving. Ideally, such a trending method can eliminate issues including deviation and ambiguity with the help of hypothetically unlimited computational power. To process data in this manner, artificial intelligence is necessary and needs to be integrated into the design process. An experiment of a design process that consists of a generative model, a data library, and a machine learning system (GAN) is introduced to demonstrate its effectiveness. The methodology is further evaluated by comparing its output with its input targets, which proves the possibility of employing machine learning systems to aggressively process data and automate the design process. Further improvement of such methodology, including judging criteria and possible applications, and the sensibility of the machine is also discussed at the end.
keywords Machine Learning, Automation, Variables, Data Processing, Sensibility, Generative Design
series ACADIA
type normal paper
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last changed 2019/12/18 08:03
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