Computational Design

Historically, designers depended on their intuition and experience to find solutions for design problems. However, with the emergence of the digital age came the newfound reliance on computers for most aspects of daily life. For designers, computational design has become the new design method relied on to find several design solutions through its ability of generating thousands of innovative, insightful, and personalized design permutations quickly. Computational design, which uses mass computing power, machine learning, and large data amounts, has made it possible to design anything for billions of people at scale, on demand, and in real-time. In fact, many predict that in the future, designers won’t only use computers to their works, but computers themselves would model, design, and print works, while designers supervise, mentor, and set parameters for these computational designs.

Architectural design also now integrates many computational design techniques, including building simulation, evolutionary optimization, and new fabrication methods, which all surpass the automation of drafting tasks. Along with their knowledge on structures, design, and building codes, architects are expected to know how to use computers and software in their profession for 3D modeling, documentation, and creating a program spreadsheet. Moreover, the increased computational capacity of tools, as well as the variety of existing computational design methods, have helped architects enhance the design process, by making it more efficient and expanding its conceptual limits. With such accessible methods, architects have also been empowered to explore and assess other complicated solutions, create and deploy advanced fabrication techniques, and significantly control different stages of the design process. In turn, this has resulted in the founding of new design approaches and terms, such as parametric design, generative design, and algorithmic design, which are terms that have long been confused with each other. Nevertheless, it is widely agreed for now, that parametric design refers to a design approach based on describing sets of designs by the use of parameters, generative design refers to a design approach that produces designs by the use of algorithms, and algorithmic design is a generative design approach that is characterized by a detectable relationship between the algorithm and its outcome.

As for computational design, it doesn’t have a fully agreed on definition, but most define it as the application of computational approaches to enhance the design process by encoding design decisions with a computer language. Most also agree that the computation-based aspects that computational design consists of include parameter setting, processing power, generative design, designing with data, and 3D modeling and visualization tools. The parameter setting tests diverse computer driven designs by applying algorithmic rule-based codes, or parametric limits, through a computer language-like logic. The processing power makes designs that are usually impossible to create by using huge amounts of cloud-based computational power and automation. The generative design uses complicated algorithms to replicate nature’s evolutionary design to create, test, and analyze many design variations. Designing with data means to create new designs through the application of big data points and strong algorithms. Complex 3D modeling and visualization tools are used by designers to test and make simulations for new ideas. Overall, most computational design environments depend on visual programming that enable designers to assemble programs graphically, instead of writing code, and its tools allow designers and architects to make their own tailored tools on their software to work how they want on their projects.

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