This article presents the design process for generating a shell-like structure from an activated bent auxetic surface through an inductive process based on applying deep learning algorithms to predict a numeric value of geometrical features. The process developed under the Material Intelligence Workflow applied to the development of (1) a computational simulation of the mechanical and physical behaviour of an activated auxetic surface, (2) the generation of a geometrical dataset composed of six geometric features with 3,000 values each, (3) the construction and training of a regression Deep Neuronal Network (DNN) model, (4) the prediction of the geometric feature of the auxetic surface's pattern distance, and (5) the reconstruction of a new shell based on the predicted value. This process consistently reduces the computational power and simulation time to produce digital prototypes by integrating AI-based algorithms into material computation design processes.

The integration of Artificial Intelligence algorithms into computational design processes promote a human-machine collaboration, transforming the role of conventional designers into meta-designer as a product of this collaboration. Specifically, StyleGAN's algorithms offer a novel approach to experimenting with shapes from previously designed images of products or objects. This article presents the application of a methodology for image experimentation to designers without previous knowledge of computation and programming. Each of the steps was developed through different cases for different speculative object production, using artificial intelligence algorithms, and reflecting -in an applied way -the designer's role as curator and cocreator of the creative process in conjunction with computing.