Data visualisation and computational (incl. parametric and generative) design research. Data visualisation and computational (including parametric and generative) design encompasses innovative techniques that use computer algorithms and modelling to create dynamic, data-driven designs. This field is crucial within Design under the Built Environment, as it enables researchers and students to explore complex design processes and optimize solutions through intelligent computation. JoVE Visualize enriches this understanding by pairing relevant PubMed articles with JoVE’s experiment videos, offering a comprehensive view of research methods and outcomes.
Key Methods & Emerging Trends
Core Methods in Parametric and Computational Design
Established methods in this field include parametric design, where algorithms define relationships between design elements, allowing adjustments to propagate automatically through models. Computational design integrates these algorithms to generate detailed visuals and analyses based on data inputs. Techniques such as CAD modelling, data visualisation, and scripting within platforms like Grasshopper or Dynamo are foundational. Researchers often explore the distinctions between parametric design vs generative design to understand how controlled parameter changes compare to autonomous design generation, thereby advancing precision and efficiency in built environment projects.
Emerging Innovations in Generative Design and AI Integration
Emerging trends increasingly focus on generative design, leveraging artificial intelligence and machine learning to produce optimized and novel solutions beyond traditional parametric limits. This includes integrating generative AI models that explore vast design possibilities and improve decision-making processes. Current research also investigates the differences between computational and generative AI, clarifying how each contributes uniquely to design automation and creativity. By combining surrogate models and active sampling, for example, building energy optimization is reimagined with data-driven strategies. These innovations highlight the evolving intersection of data visualisation, parametric, and generative frameworks within design research.