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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

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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 2318-6968, DOI 10.5151/proceedings-ecaadesigradi2019_602

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