Dezembro 2019 vol. 7 num. 1 - 37 Education and Research in Computer Aided Architectural Design in Europe and XXIII Iberoamerican Society of Digital Graphics, Joint Conference (N. 1)
Article - Open Access.
Steps towards AI augmented parametric modeling systems for supporting design exploration
Steps towards AI augmented parametric modeling systems for supporting design exploration
Toulkeridou, Varvara ;
Article:
Dataflow parametric modeling environments have become popular asexploratory tools due to them allowing the variational exploration of a design bycontrolling the parameters of its parametric model schema. However, the natureof these systems requires designers to prematurely commit to a structure andhierarchy of geometric relationships, which makes them inflexible when it comesto design exploration that requires topological changes to the parametricmodeling graph. This paper is a first step towards augmenting parametricmodeling systems via the use of machine learning for assisting the user towardstopological exploration. In particular, this paper describes an approach whereLong Short-Term Memory recurrent neural networks, trained on a data set ofparametric modeling graphs, are used as generative systems for suggestingalternative dataflow graph paths to the parametric model under development.
Article:
Dataflow parametric modeling environments have become popular asexploratory tools due to them allowing the variational exploration of a design bycontrolling the parameters of its parametric model schema. However, the natureof these systems requires designers to prematurely commit to a structure andhierarchy of geometric relationships, which makes them inflexible when it comesto design exploration that requires topological changes to the parametricmodeling graph. This paper is a first step towards augmenting parametricmodeling systems via the use of machine learning for assisting the user towardstopological exploration. In particular, this paper describes an approach whereLong Short-Term Memory recurrent neural networks, trained on a data set ofparametric modeling graphs, are used as generative systems for suggestingalternative dataflow graph paths to the parametric model under development.
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DOI: 10.5151/proceedings-ecaadesigradi2019_602
Referências bibliográficas
- [1] .
Como citar:
Toulkeridou, Varvara; "Steps towards AI augmented parametric modeling systems for supporting design exploration", p. 81-92 . In: Proceedings of 37 eCAADe and XXIII SIGraDi Joint Conference, “Architecture in the Age of the 4Th Industrial Revolution”, Porto 2019, Sousa, José Pedro; Henriques, Gonçalo Castro; Xavier, João Pedro (eds.).
São Paulo: Blucher,
2019.
ISSN 2318-6968,
DOI 10.5151/proceedings-ecaadesigradi2019_602
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