| id |
ecaade2024_300 |
| authors |
Sebestyen, Adam; Özdenizci, Ozan; Legenstein, Robert; Hirschberg, Urs |
| year |
2024 |
| title |
AI-Infused Design: Merging parametric models for architectural design |
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 489498 |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.489
|
| summary |
This paper presents ongoing work on developing 3D Generative AI tools based on parametric models to facilitate novel types of Design Space Exploration (DSE) to overcome human biases and expand the range of feasible design solutions. By integrating parametric models and neural networks, the study demonstrates how 3D-mesh based datasets generated from different parametric models can be combined in deep learning to create more diverse design spaces. Specifically, we compare training on the same datasets with an unconditioned Variational Autoencoder (VAE) and with conditioned Denoising Diffusion Models (DDMs). We present a novel approach of mixing DDM design spaces and contrast this method with our previous work using a VAE. The paper compares the outputs of VAE and DDMs, highlighting their respective strengths and weaknesses, and proposes a hybrid generative AI model combining both approaches to harness their complementary advantages. |
| keywords |
Deep Learning, VAE, Denoising Diffusion Models, Parametric Design, Design Space Exploration |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (692,879 bytes) |
| references |
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