The difference between generative design and parametric design

Generative design and parametric design are subsets of computational design, which uses input parameters and constraints in the process of advancing a design to a preferred outcome.

The Difference

Parametric design uses parameters and constraints to solve a design problem, while generative design applies algorithms to those same parameters to generate hundreds or thousands of possible design variations to review and choose from.

Parametric Design

The term parametricism was established by the architectural theorist Patrick Schumacher in 2008. A partner at Zaha Hadid Architects, Schumacher published a series of works that defined and explained the core tenets of parametricism. This design style is exceptionally diverse in its expression of form, focusing on developing spatial relationships between elements and forms. The design style is made possible by the process of parametric design.

In its simplest form, parametric design is an interactive and iterative process that contains a set of parameters and a set of outcomes. The parameters are inputted by the user and, as they are adjusted and manipulated, the design will follow suit.  

Using parametric design, a designer or engineer can make changes to the project in real-time, and the model is updated automatically. In this way, a designer can explore many options before choosing a final design.

Parametric design is faster than traditional design, reducing design work from weeks to mere days. It enables a design team to test multiple solutions and save certain designs for reuse on later projects.

While parametric design has been used in the gaming and movie industries for a while, it continues to gain popularity in other areas, such as industrial and architectural design.

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Generative Design

Generative design is a design optimization technique. Often referred to as a co-design process, a designer introduces parameters and constraints, while the system uses algorithms to perform the optimization process in pursuit of the best solution.

Along with inputting the parameters and constraints, the designer will establish the evaluation metrics to rank the results.

To get the desired results, the designer must be properly educated in inputting the precise constraints and parameters in order to get good output. Once the inputs and success metrics have been established, the system will run to calculate its first results. Using the metrics provided by the user, the results are ranked in order of desirability and fit, rejecting the poor solutions. Then, using what it learned, it chooses the features with the highest scores to create another set of results. Using this iterative approach, designers can test thousands of options, while also learning about risks for each design of the project.

 

Together

Parametric and generative design have a big role to play in the future of design.

In addition to saving time and increasing productivity, they are a reliable way to lower project costs and anticipate risk. The two concepts build on each other, as both require a human designer to input the required parameters and constraints and establish the success metrics. In a world where we are asked to do more with less, both can improve the process of designing projects and giving the customer precisely what they want.

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