Abstract
This paper introduces concepts and computational methodologies for utilizing neural networks as design tools for architecture and demonstrates their application in the making of doubly curved metal surfaces using a contemporary version of the English Wheel. The research adopts an interdisciplinary approach to develop a novel method to model complex geometric features using computational models that originate from the field of computer vision.
The paper contextualizes the approach with respect to the current state of the art of the usage of artificial neural networks both in architecture and beyond. It illustrates the cyber physical system that is at the core of this research, with a focus on the employed neural network–based computational method. Finally, the paper discusses the repercussions of these design tools on the contemporary design paradigm.
The paper contextualizes the approach with respect to the current state of the art of the usage of artificial neural networks both in architecture and beyond. It illustrates the cyber physical system that is at the core of this research, with a focus on the employed neural network–based computational method. Finally, the paper discusses the repercussions of these design tools on the contemporary design paradigm.
Original language | English |
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Title of host publication | ACADIA // 2018: Recalibration. On imprecisionand infidelity : Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture |
Number of pages | 9 |
Publication date | Oct 2019 |
Pages | 146-155 |
ISBN (Electronic) | 978-0-692-17729-7 |
Publication status | Published - Oct 2019 |
Event | ACADIA 2018: Recalibration. On imprecision and infidelity - Universidad Iberoamericana, Mexico City, Mexico Duration: 18 Oct 2018 → 20 Oct 2018 http://2018.acadia.org/schedule.html |
Conference
Conference | ACADIA 2018 |
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Location | Universidad Iberoamericana |
Country/Territory | Mexico |
City | Mexico City |
Period | 18/10/2018 → 20/10/2018 |
Internet address |
Keywords
- machine learning
- cyber physical systems
- digital design
- robotic fabrication
Artistic research
- No