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Generative Design

Generative Design

An exploration of how generative design technologies can impact the work of design.

I think there’s an interesting question when you’re looking at generative design about where creativity comes from. Does creativity come from a human, or does creativity come from how humans work with technology in different kinds of ways? Could a computer suggest options to you that never might have come to mind or make you think very differently about a problem?
—Molly Wright Steenson

Objective & Outcomes

With the increasing power and availability of generative technologies in recent years, such as ChatGPT, Dall-E 2, and Midjourney, the relationship between the designer and their computational tools is becoming increasingly collaborative, interdependent, and complicated.

This course is a broad introduction to methods, tools, and tensions associated with contemporary generative design, which empowers designers to collaborate with artificial intelligence (AI) and algorithms in their design process. Structured around the design opportunities found in customizing design outcomes to specific contexts, students experiment with how computational tools allow designers to craft algorithms that, in turn, create unique products, services, platforms, and experiences matched to each potential user, context, and environment. Students work through a series of exercises and a final project exploring how data collected on individual scenarios and powerful algorithms can be integrated into every aspect of the design process — not only in insight discovery and research process — but also in the direct shaping of final design interventions.

To realize their concepts, students are exposed throughout the course to a variety of algorithms and artificial intelligence, as well as digital fabrication tools, including 3D printing, laser cutting, and CNC machining, alongside the implications, limitations, and capabilities of contemporary and near-future digital manufacture. The choice of fabrication technologies and strategies will vary based on student interest and resource availability.

Upon completing the course, students will be oriented to emerging technologies involved with generative design and able to evaluate their usefulness in different design contexts.

Typical Schedule

  • Session 1: Generative Design: Introduction
  • Session 2: Generative Art: Designing with instructions, randomness, & relationships
  • Session 3: Generative Music: Designing with patterns, palettes, & loops
  • Session 4: Generative Fashion: Designing for anthropometry & computational anatomy
  • Session 5: Generative Objects: Designing for parametric relationships
  • Session 6: Generative Spaces: Designing with physical & behavioral simulations
  • Session 7: Generative Poetry, Narrative, & Policy
  • Session 8: Generative Pattern: Designing with Voronoi, Delaunay, & routing algorithms
  • Session 9: Generative Structure: Designing with formal grammars & growth algorithms
  • Session 10: Generative Evolution: Designing with evolutionary solvers & life
  • Session 11: Generative Inference: Designing with machine learning & neural networks
  • Session 12: Generative Creativity: Designing with generative adversarial networks
  • Session 13: Generative Impact
  • Session 14: Final Review & Discussion