UG: 4.550 G: 4.570

Class website

Provides students with an opportunity to explore projects that engage real world problems concerning spatial design, technology, media, and society. In collaboration with industry partners and public institutions, students identify topical issues and problems, and also explore and propose solutions through the development of new ideas, theories, tools, and prototypes. Industry and academic collaborators act as a source of expertise, and as clients and critics of projects developed during the term. General theme of workshop varies by semester or year. Open to students from diverse backgrounds in architecture and other design-related areas.

Additional work required of students taking graduate version.

4.542:

An advanced examination of the shape grammar formalism and its relationship to some key issues in a variety of other fields, including art and design, philosophy, history and philosophy of science, linguistics and psychology, literature and literary studies, logic and mathematics, and artificial intelligence. Student presentations and discussion of selected readings are encouraged. Topics vary from year to year.

4.582:

In-depth presentations of current research in design and computation.

An in-depth introduction to shape grammars and their applications in architecture and related areas of design. Shapes in the algebras Ui j, in the algebras Vi j and Wi j incorporating labels and weights, and in algebras formed as composites of these. Rules and computations. Shape and structure. Designs.

Topics vary from year to year. Can be repeated with permission of instructor.

UG: 4.520 G: 4.521

Introduces a visual-perceptual, rule-based approach to design using shape grammars. Covers grammar fundamentals through lectures and in-class, exercises. Focuses on shape grammar applications, from stylistic analysis to creative design, through presentations of past applications and through short student exercises and projects. Presents computer programs for automating shape grammars.

Additional work required of students taking graduate version.

UG: 4.507 G: 4.567

Addresses fundamental methods, theories, and practices that engage contemporary modeling tools in the context of architectural design. Introduces selected academic and professional topics through lectures, demonstrations, and assignments. Topics include parametric modeling, component types and assembly, prototyping, scripting, and simulations. Initiates intellectual explorations in the use of building information modeling in research projects and design practices.

Additional work required of students taking graduate version.

See class website.

UG: 4.501; Grad: 4.511

This course introduces digital fabrication as a method of home, hut, and shelter delivery (construction). The class explores industrial-based building production from prefab to digital fab. Most important we explore new computational techniques for rapid construction. We will explore the basics of tiny building design, different types of 3D modeling systems, scalable ways to prototype and CNC fabrication. The focus is on learning by doing. The class result will be a well-designed prototype of a small building as a single packaged product. 

Additional work required of graduate students.

Selection of thesis topic, definition of method of approach, and preparation of thesis proposal. Independent study supplemented by individual conference with faculty.

Selection of thesis topic, definition of method of approach, and preparation of thesis proposal. Independent study supplemented by individual conference with faculty.

Focuses on applications of machine learning (ML) for creative design generation and data-informed design exploration, with an emphasis on visual and 3-D generative systems. Explores how recent advances in artificial intelligence, and specifically machine learning, can offer humans more natural, performance-driven design processes. Covers a wide range of machine learning algorithms and their applications to design, with topics including neural networks, generative adversarial networks, variational autoencoders, dimensionality reduction, geometric deep learning, and other ML techniques. Includes an open-ended, applied research or design project demonstrating an original, creative use of machine learning for design, architecture, engineering, or art. 

UG: 4.440, 1.056; Grad: 4.462

Introduces the design and behavior of large-scale structures and structural materials. Emphasizes the development of structural form and the principles of structural design. Presents design methods for timber, masonry, concrete and steel applied to long-span roof systems, bridges, and high-rise buildings. Includes environmental assessment of structural systems and materials. In laboratory sessions, students solve structural problems by building and testing simple models. 

Graduate and undergraduate students have separate lab sections.

GIR LAB (4.440)