| id |
ecaadesigradi2019_602 |
| authors |
Toulkeridou, Varvara |
| year |
2019 |
| title |
Steps towards AI augmented parametric modeling systems for supporting design exploration |
| source |
Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 81-90 |
| doi |
https://doi.org/10.52842/conf.ecaade.2019.1.081
|
| summary |
Dataflow parametric modeling environments have become popular as exploratory tools due to them allowing the variational exploration of a design by controlling the parameters of its parametric model schema. However, the nature of these systems requires designers to prematurely commit to a structure and hierarchy of geometric relationships, which makes them inflexible when it comes to design exploration that requires topological changes to the parametric modeling graph. This paper is a first step towards augmenting parametric modeling systems via the use of machine learning for assisting the user towards topological exploration. In particular, this paper describes an approach where Long Short-Term Memory recurrent neural networks, trained on a data set of parametric modeling graphs, are used as generative systems for suggesting alternative dataflow graph paths to the parametric model under development. |
| keywords |
design exploration; visual programming; machine learning |
| series |
eCAADeSIGraDi |
| email |
|
| full text |
 file.pdf (4,139,446 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:58 |
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