Generative Design Software

Software powered by an AI algorithm capable of autonomously generating design solutions based on specific goals and restrains.
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Technology Life Cycle

Technology Life Cycle

Growth

Marked by a rapid increase in technology adoption and market expansion. Innovations are refined, production costs decrease, and the technology gains widespread acceptance and use.

Technology Readiness Level (TRL)

Technology Readiness Level (TRL)

Prototype Demonstration

Prototype is fully demonstrated in operational environment.

Technology Diffusion

Technology Diffusion

Early Adopters

Embrace new technologies soon after Innovators. They often have significant influence within their social circles and help validate the practicality of innovations.

Generative Design Software

An AI-enabled software that designs objects autonomously, following preprogrammed parameters, such as goals and restrains, set by the user. Examples of parameters range from maximum manufacturing costs, material, desired function, weight, or other factors relevant to the user.

The software can generate a series of designs that meet the initial criteria, leaving the human designer to choose, change parameters, implement new standards, or use it as a starting point for new creations. This technological solution is essentially iterative and collaborative between humans and machines.

By creating models using advanced mathematical calculations, this process has proven to reduce material usage and optimize efficiency. Also, instead of right angles, the algorithm often comes up with curved, rounded shapes. These shapes look more like the shapes seen in nature because these design rules follow the forms of the natural world.

Future Perspectives

"If I have seen further than others, it is by standing upon the shoulders of giants." This famous Isaac Newton sentence reveals both a blessing and a curse of the human condition. If, on the one hand, we can communicate, keep information and build on other people's achievements, contrarily, it is hard to challenge pre-established assumptions or to "unlearn" how to do things, to be able to do it differently. Generative design tools could free humanity from this curse, enabling us to create completely fresh designs that are not what we are used to seeing.

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Sources
AI-driven design, combined with advanced manufacturing methods and materials, can fully deliver on the promises of modern production.
Smart algorithms won’t just lead to better products--they could redefine how product development is done.
Generative Adversarial Networks Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio (Submitted on 10 Jun 2014) We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.

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2011 — 2024