Computational Design & Optimization for Buildings

Catalog description

This course focuses on emerging computational approaches for creative multi-factor parametric research, early-stage design and optimization of buildings. It begins with an overview of the principles of parametric design and visual programming before covering strategies for design space exploration and optimization in depth. Topics include computational design fundamentals such as problem parameterization and formulation; heuristic, gradient-based, and interactive optimization; multi-objective optimization; surrogate modeling; and data visualization for decision-making. Beyond traditional optimization, it highlights ways in which elements of a building design optimization problem (variables, the geometric definition, constraints, objectives) can be interactively interpreted, manipulated, and connected, often using data science techniques, to provide effective feedback and guidance during building-related research and design, which can include both qualitative and quantitative goals. Although the theory of these subjects is described, the emphasis is on how they can be used in research to improve the design and construction process.