| id |
ecaade2021_186 |
| authors |
Sebestyen, Adam, Rock, Johanna and Hirschberg, Urs Leonhard |
| year |
2021 |
| title |
Towards Abductive Reasoning-Based Computational Design Tools - Using Machine Learning as a way to explore the combined design spaces of multiple parametric models |
| doi |
https://doi.org/10.52842/conf.ecaade.2021.1.141
|
| source |
Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 141-150 |
| summary |
Abductive Reasoning - reasoning based on experience - is important for design. This research tries to lay the groundwork for using Variational Autoencoders (VAE) - currently one of the most established deep learning architectures for generative modelling, a subfield of Machine Learning (ML) - as a way to support abductive reasoning in early design stages. While our research is still in its early stages, the first results look promising. In this paper we explain the current state of our research, its premises and methods, and discuss the results achieved thus far. We also explain the motivation behind our work and the potential we see in using VAE in this way and why we believe our approach could represent a paradigmatic shift in the way parametric models can be used in design. |
| keywords |
Machine Learning; Parametric Design; Variational Autoencoders; Generative Modeling; Abductive Reasoning |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (2,921,067 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 08:00 |
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