Social Computational Design Method for Generating Product Shapes with GAN and Transformer Models
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Abstract (arXiv)
A social computational design method is established, aiming at taking advantages of the fast-developing artificial intelligence technologies for intelligent product design. Supported with multi-agent system, shape grammar, Generative adversarial network, Bayesian network, Transformer, etc., the method is able to define the design solution space, prepare training samples, and eventually acquire an intelligent model that can recommend design solutions according to incomplete solutions for given design tasks. Product shape design is used as entry point to demonstrate the method, however, the method can be applied to tasks rather than shape design when the solutions can be properly coded.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 10 references that are in the map. First posted on arXiv 22 Feb 2022.
Builds on
4 of 10 references.
References · 4
- 2021Deep Generative Models in Engineering Design: A Reviewcited by 105
- 2021Computer-Aided Design as Languagecited by 82
- 2020Sketchformer: Transformer-based Representation for Sketched Structurecited by 78
- 2020Integrating Deep Learning into CAD/CAE System: Generative Design and Evaluation of 3D Conceptual Wheelcited by 25
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Citing papers per year: 2023: 1.
1 paper.