| id |
acadia22_652 |
| authors |
Saunders, Andrew; Studebaker, Riley; Moriarty, Claire Eileen |
| year |
2022 |
| title |
Deep Relief |
| source |
ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 652-661. |
| summary |
Deep Relief combines generative artificial neural network computational methods with industrial robotic manufacturing procedures to explore the potential of designing, fabricating, and expressing novel part to whole relationships at an architectural scale. More specifically, the relief tests the capacity of convolutional neural networks (CNNs) to generate a language of three-dimensional geometry to drive tool specific robotic fabrication at the intersection of painterly representation and non-authorial composition. |
| series |
ACADIA |
| type |
paper |
| email |
|
| full text |
 file.pdf (7,147,502 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/02/06 14:04 |
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