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dc.contributor.authorCarta, Silvio
dc.date.accessioned2021-08-12T14:15:02Z
dc.date.available2021-08-12T14:15:02Z
dc.date.issued2021-07-23
dc.identifier.citationCarta , S 2021 , ' Self-Organizing Floor Plans ' , Harvard Data Science Review HDSR , vol. 3 . https://doi.org/10.1162/99608f92.e5f9a0c7
dc.identifier.issn2644-2353
dc.identifier.otherPURE: 25760548
dc.identifier.otherPURE UUID: 17f432dd-8546-4a3f-9dc1-f2b416397058
dc.identifier.urihttp://hdl.handle.net/2299/24977
dc.description© 2021 by the author(s). The editorial is licensed under a Creative Commons Attribution (CC BY 4.0) International license (https://creativecommons.org/licenses/by/4.0/legalcode)
dc.description.abstractThis article introduces and comments on some of the techniques currently used by designers to generate automatic building floor plans and spatial configurations in general, with emphasis on machine learning and neural networks models. This is a relatively new tendency in computational design that reflects a growing interest in advanced generative and optimization models by architects and building engineers. The first part of this work contextualizes self-organizing floor plans in architecture and computational design, highlighting their importance and potential for designers as well as software developers. The central part discusses some of the most common techniques with concrete examples, including Neuro Evolution of Augmenting Topologies (NEAT) and Generative Adversarial Networks (GAN). The final section of the article provides some general comments considering pitfalls and possible future developments, as well as speculating on the future of this trend.en
dc.format.extent39
dc.language.isoeng
dc.relation.ispartofHarvard Data Science Review HDSR
dc.titleSelf-Organizing Floor Plansen
dc.contributor.institutionSchool of Creative Arts
dc.contributor.institutionArt and Design
dc.contributor.institutionDesign Research Group
dc.contributor.institutionZero Carbon Lab
dc.contributor.institutionCentre for Climate Change Research
dc.contributor.institutionCentre for Future Societies Research
dc.description.statusPeer reviewed
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1162/99608f92.e5f9a0c7
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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