| id |
caadria2024_531 |
| authors |
Mottaghi, Esmaeil, Abuzuraiq, Ahmed M. and Erhan, Halil |
| year |
2024 |
| title |
D-Predict: Integrating Generative Design and Surrogate Modelling with Design Analytics |
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 455–464 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.1.455
|
| summary |
The increasing importance of performance prediction in architecture has driven designers to incorporate computational tools like generative design and building simulations to widen and guide their exploration. However, these tools pose their own challenges; specifically, simulations can be computationally demanding and generative design leads to large design spaces that are hard to navigate. To address those challenges, this paper explores integrating machine learning-based surrogate modelling, interactive data visualisations, and generative design. D-Predict, a prototype, features the generation, management and comparison of design alternatives aided with surrogate models of daylighting and energy. |
| keywords |
generative design, building performance assessment, surrogate modelling, machine learning, design analytics |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,434,035 bytes) |
| references |
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2024/11/17 22:05 |
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