%0 Conference Paper %1 karaman_augmenting_2021 %A Karaman, Ridvan %A Dong, Zhetao %A Drachenberg, Kurt %A Rinderspacher, Katja %A Zechmeister, Christoph %A Oguz, Ozgur %A Menges, Achim %B Realignments: Toward Critical Computation - ACADIA 2021 %D 2021 %E Farahi, Behnaz %E Bogosian, Biayna %E Scott, Jane %E García del Castillo y López, Jose Luis %E Dörfler, Kathrin %E Grant, June A. %E Parascho, Stefana %E Noel, Vernelle A. A. %K %T Augmenting Design: Solving design problems using generative deep learning frameworks with multiple objectives %X In recent years, generative machine learning methods such as variational autoencoders (VAEs) and generative adversarial networks (GANs) have opened up new avenues of exploration for architects and designers. The presented work explores how these methods can be expanded by incorporating multiple abstract criteria directly into the formulation of the algorithm that negotiates these complex criteria and proposes a fitting design. It draws inspiration from the works of several design theorists who have developed such goal-oriented approaches to design, and sets up multiple-objective VAE and GAN frameworks with this idea in mind. The research demonstrates that by incorporating multiple constraints using auxiliary discriminator networks, the developed algorithms are able to generate innovative solutions to two example problems: the design of 2D digits, and the design of 3D voxel chairs. By speculating and examining the role of the designer in data based generative computational design workflows, the research aims to provide an approach for solving design tasks in the age of big data.

@inproceedings{karaman_augmenting_2021, abstract = {In recent years, generative machine learning methods such as variational autoencoders (VAEs) and generative adversarial networks (GANs) have opened up new avenues of exploration for architects and designers. The presented work explores how these methods can be expanded by incorporating multiple abstract criteria directly into the formulation of the algorithm that negotiates these complex criteria and proposes a fitting design. It draws inspiration from the works of several design theorists who have developed such goal-oriented approaches to design, and sets up multiple-objective VAE and GAN frameworks with this idea in mind. The research demonstrates that by incorporating multiple constraints using auxiliary discriminator networks, the developed algorithms are able to generate innovative solutions to two example problems: the design of 2D digits, and the design of 3D voxel chairs. By speculating and examining the role of the designer in data based generative computational design workflows, the research aims to provide an approach for solving design tasks in the age of big data.}, added-at = {2024-05-27T08:58:44.000+0200}, author = {Karaman, Ridvan and Dong, Zhetao and Drachenberg, Kurt and Rinderspacher, Katja and Zechmeister, Christoph and Oguz, Ozgur and Menges, Achim}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c4b7a25761de5a93e0aa1c00a2180e93/romane2023}, booktitle = {Realignments: {Toward} {Critical} {Computation} - {ACADIA} 2021}, editor = {Farahi, Behnaz and Bogosian, Biayna and Scott, Jane and García del Castillo y López, Jose Luis and Dörfler, Kathrin and Grant, June A. and Parascho, Stefana and Noel, Vernelle A. A.}, interhash = {c75a2dd6de21be6d0aaab6726612b25b}, intrahash = {c4b7a25761de5a93e0aa1c00a2180e93}, keywords = {}, shorttitle = {Augmenting {Design}}, timestamp = {2024-05-27T08:58:44.000+0200}, title = {Augmenting {Design}: {Solving} design problems using generative deep learning frameworks with multiple objectives}, year = 2021 }