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.

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.

Palavras-chave:

DOI: 10.5151/proceedings-ecaadesigradi2019_602

Referências bibliográficas

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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 23186968, DOI 10.5151/proceedings-ecaadesigradi2019_602