Introduces advanced topics in building simulation for design and control of envelope and thermal systems for architects, engineers, environmental consultants, and beyond. Students will gain conceptual knowledge and technical skills to drive design decisions based on environmental performance. The focus of final design projects will be to reduce operational energy usage and carbon intensity. Course format will include a combination of traditional lectures, hands-on exercises, and design project development.

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)

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.

Fundamental research methodologies and ongoing investigations in building tehnology to support the development of student research projects. Topics drawn from low energy building design and thermal comfort, building systems analysis and control, daylighting, structural design and analysis, novel building materials and construction techniques and resource dynamics. Organized as a series of two- and three-week sessions that consider topics through readings, discussions, design and analysis projects, and student presentations.