| id |
caadria2023_38 |
| authors |
Xu, Qingru Mirah, Garcia del Castillo Lopez, Jose Luis and Samuelson, Holly Wasilowski |
| year |
2023 |
| title |
Towards a Decision Framework Integrating Physics-Based Simulation and Machine Learning in Conceptual Design |
| source |
Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 371–380 |
| doi |
https://doi.org/10.52842/conf.caadria.2023.2.371
|
| summary |
Researchers have leveraged machine learning technologies and physics-based modelling and simulation techniques to generate fast predictions of factors relevant to building daylighting, energy use, and other performance metrics. However, in the current literature, there is no generalized method outlining the thought process behind whether to implement physics-based simulation, machine learning, or both methods. This paper first proposes a conceptual framework that identifies the considerations researchers might ask when developing their workflow. Second, it presents an example case study developed according to the framework. The case study used daylight simulation and parametric modelling software to generate synthetic data automatically to train a conditional generative adversarial framework. The model was hosted on an interactive web app allowing users to create their building designs and provide design performance metrics and improved design simultaneously. |
| keywords |
Physic-based Modelling and Simulation, Physics-based Machine Learning, Early Design, Architecture, Research Development |
| series |
CAADRIA |
| email |
mirahxu@gsd.harvard.edu |
| full text |
 file.pdf (922,809 bytes) |
| references |
Content-type: text/plain
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| last changed |
2023/06/15 23:14 |
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