MAA01 23/24 Computational Design I Seminar Level 2

Credits: Uri Lewis Torres, Introductory Studio G2, 2020/21

In computer science, algorithms are habitually defined as fixed and open finite procedures of step-by-step instructions understood to produce something other than themselves. Structures of logics interfacing with Data, sourced from any computable phenomena. In this course, we will focus on emergent design strategies based on algorithmic design logics. From the physical spaces of our built environment to the networked spaces of digital culture, algorithmic and computational strategies are reshaping not only design strategies but the entire perception of Architecture and its boundaries.

In this course seminar we will focus on understanding logics and computational design workflows that can lead to advanced algorithmic thinking. This course aims to solidify the ground of the basics of grasshopper while amplifying already existing concepts into more advanced notions that can be put into practice. 

Main tools of the course will be McNeel’s Rhinoceros v7 and Grasshopper3d. As a complementary tool for Rhino we’ll focus on the associative design platform of Grasshopper3d, a graphical algorithm editor rightly integrated with Rhino’s 3D modelling tools. Moreover, during the course we will adopt various plugins implementing Grasshopper’s features and its capabilities, in order to achieve full control of complex design strategies.

TERM 1 

In the realm of computational design and parametric architecture, Term 1 is structured with a strong emphasis on the following key objectives:

1. Decoding Existing Projects and Workflow Analysis: The primary focus is on dissecting and reverse engineering existing architectural projects. This process allows students to gain profound insights into the intricate procedural workflows that underlie these designs.
2. Harnessing Parametric Design Principles: Students will actively apply parametric design principles to create their unique design workflows and visual representations. This entails the utilisation of algorithms and parameters to craft innovative architectural solutions.
3. Mastery of Advanced Parametric Design: The curriculum delves deep into the realm of advanced parametric design techniques. Students will develop a comprehensive understanding of digital tectonics and are encouraged to cultivate their distinctive styles and design methodologies within the domain of data-driven design.
4. Exploration of Real-World Scenarios: Students will be actively engaged in exploring practical scenarios where they can effectively employ the principles of data-driven design and emerging computational techniques. This exploration empowers them to transform abstract conceptualizations into tangible, comprehensible architectural manifestations.
5. Fostering Versatility: The ultimate objective is to nurture a profound comprehension of data-driven design that can be flexibly applied across a diverse array of architectural contexts, enabling students to achieve specific design goals effectively.

In summary, Term 1 in the context of computational design and parametric architecture revolves around reverse engineering existing projects, the application of advanced parametric design principles, practical exploration, and, ultimately, cultivating a versatile understanding of data-driven design to craft diverse architectural systems.

Learning Objectives

At course completion the student will:

• Understand fundamental concepts of computational design;
• Learn how to create strategies to build algorithms;
• Have knowledge of basic concepts of generative design;
• Be capable of generating parameterized processes;
• Apply data driven design logics;
• Learn how to create dynamic modelling;
• Obtain a deeper knowledge of algorithmic design concepts and parametrization of geometry
• Obtain a deeper knowledge of data management in grasshopper
• Learn in deep about the parameterization of complex geometries
• Learn about the most recent workflows for complex modelling
• Understand the notions and practical use of optimization algorithms