| id |
ijac201816407 |
| authors |
Mahankali, Ranjeeth; Brian R. Johnson and Alex T. Anderson |
| year |
2018 |
| title |
Deep learning in design workflows: The elusive design pixel |
| source |
International Journal of Architectural Computing vol. 16 - no. 4, 328-340 |
| summary |
The recent wave of developments and research in the field of deep learning and artificial intelligence is causing the border between the intuitive and deterministic domains to be redrawn, especially in computer vision and natural language processing. As designers frequently invoke vision and language in the context of design, this article takes a step back to ask if deep learning’s capabilities might be applied to design workflows, especially in architecture. In addition to addressing this general question, the article discusses one of several prototypes, BIMToVec, developed to examine the use of deep learning in design. It employs techniques like those used in natural language processing to interpret building information models. The article also proposes a homogeneous data format, provisionally called a design pixel, which can store design information as spatial-semantic maps. This would make designers’ intuitive thoughts more accessible to deep learning algorithms while also allowing designers to communicate abstractly with design software. |
| keywords |
Associative logic, creative processes, deep learning, embedding vectors, BIMToVec, homogeneous design data format,
design pixel, idea persistence |
| series |
journal |
| email |
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| full text |
 file.pdf ( bytes) |
| references |
Content-type: text/plain
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| last changed |
2019/08/07 14:04 |
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