# DeepCloud. The Application of a Data-driven, Generative Model in Design

arXiv: https://arxiv.org/abs/1904.01083 · HTML: https://mlsanity.com/paper/1904.01083/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Inverse airfoil design method for generating varieties of smooth airfoils using conditional WGAN-gp](https://mlsanity.com/paper/2110.00212/) (2021)
- [AIGenC: An AI generalisation model via creativity](https://mlsanity.com/paper/2205.09738/) (2022)
- [AI and the creative realm: A short review of current and future applications](https://mlsanity.com/paper/2306.01795/) (2023)

## Abstract (arXiv)

Generative systems have a significant potential to synthesize innovative design alternatives. Still, most of the common systems that have been adopted in design require the designer to explicitly define the specifications of the procedures and in some cases the design space. In contrast, a generative system could potentially learn both aspects through processing a database of existing solutions without the supervision of the designer. To explore this possibility, we review recent advancements of generative models in machine learning and current applications of learning techniques in design. Then, we describe the development of a data-driven generative system titled DeepCloud. It combines an autoencoder architecture for point clouds with a web-based interface and analog input devices to provide an intuitive experience for data-driven generation of design alternatives. We delineate the implementation of two prototypes of DeepCloud, their contributions, and potentials for generative design.