Artificial Intelligence | Genesis Lab

Augmented Computational Design; Methodical Application of Artificial Intelligence in Generative Design

Mon, 01 Jan 0001 00:00:00 +0000

Authors: Pirouz Nourian, Shervin Azadi, Roy Uijtendaal, Nan Bai

Abstract:

This chapter presents methodological reflections on the necessity and utility of artificial intelligence in generative design. Specifically, the chapter discusses how generative design processes can be augmented by AI to deliver in terms of a few outcomes of interest or performance indicators while dealing with hundreds or thousands of small decisions. The core of the performance-based generative design paradigm is about making statistical or simulation-driven associations between these choices and consequences for mapping and navigating such a complex decision space. This chapter will discuss promising directions in Artificial Intelligence for augmenting decision-making processes in architectural design for mapping and navigating complex design spaces.

Collective Intelligence in Generative Design; A Human-Centric Approach Towards Scientific Design

Mon, 01 Jan 0001 00:00:00 +0000

Authors: Shervin Azadi, Pirouz Nourian

Abstract:

Mathematical formalization of knowledge within a scientific paradigm unifies sporadic efforts through converging glossary and notation, thus enabling scientists to identify knowledge gaps and discrepancies easier. Furthermore, comprehensive formalization reveals potential bridges to various domain sciences and facilitates the utilization of methods that have proven effective in scientific problem-solving. In the case of Architecture and Built Environment, there is a long history of scattered efforts for identifying and formalizing design problems and design methodologies, but the big picture is yet missing. In this short piece, we name and frame some of these efforts to identify their parallels with Mathematics, Computer Science, and Systems Theory, as well as to illustrate new opportunities that methodical design unlocks.

Generative Design in Architecture; From Mathematical Optimization to Grammatical Customization

Mon, 01 Jan 0001 00:00:00 +0000

Authors: Pirouz Nourian, Shervin Azadi, Robin Oval

Abstract: This chapter provides a methodological overview of generative design in architecture, especially highlighting the commonalities between three separate lineages of generative approaches in architectural design, namely the mathematical optimization methods for topology optimization and shape optimization, generative grammars (shape grammars and graph grammars), and [agent-based] design games. A comprehensive definition of generative design is provided as an umbrella term referring to the mathematical, grammatical, or gamified methodologies for systematic synthesis, i.e. derivation, itemization, or exploration of configurations. Among other points, it is shown that generative design methods are not necessarily meant to automate design; but rather provide structured mechanisms to facilitate participatory design or creative mass customization. Effectively, the chapter provides the theoretical minimum for understanding generative design as a paradigm in computational design; demystifies the term generative design as a technological hype; shows a precis of the history of the generative approaches in architectural design; provides a minimalist methodological framework summarising lessons from the three lineages of generative design; and deepens the technological discourse on generative design methods by reflecting on the topological constructs and techniques required for devising generative systems or design machines, including those equipped with Artificial Intelligence. Moreover, the notions of discrete design and design for discrete assembly are discussed as precursors to the core concept of design as decision-making in generative design, thus hinting to avenues of future research in manufacturing-informed combinatorial masscustomization and discrete architecture in tandem with generative design methods.

AI Configurators

Sat, 06 Feb 2021 16:49:47 +0000

Background and aim:

Spatial qualities heavily depend on the configuration of spaces. In this research project, we develop computational agents that configures the space within a voxelated envelope while ensuring spatial qualities such as daylight, accessibility to other spaces, etc., via Multi-Criteria Decision Analysis. Each computational agent utilizes Reinforcement Learning to understand the inter-relation of global spatial quality criteria with local spatial decisions.

Research question:

How to train an ensemble of artificial agents to make local spatial decisions to attain high global spatial qualities?

Design objective:

To design and implement a computational 3D layout methodology using DRL.

Methods:

Deep Reinforcement Learning (DRL, Artificial Intelligence)

Multi-Criteria Decision Analysis (MCDA)

Topology Optimization

Computer Programming (Python/C#)

Augmented Urban Planning

Sat, 06 Feb 2021 16:49:47 +0000

Space Optimization

Sat, 06 Feb 2021 16:49:47 +0000

Background and aim:

The spatial configuration of a building can be abstracted as a network of functional spaces that influences the functionality of a building in terms of movements of the occupants, logistic efficiency, feasibility of social distancing, safety of routing, and security. The aim of the research is to propose a systematic way of configuring buildings in 3D to optimally meet functional requirements pertaining to such factors etc.

Research question:

How to optimize the allocation of spaces in a building based on a program of requirements, and a set of criteria concerning accessibility and visibility?

Design objective:

To design and implement a computational 3D layout methodology.

Methods:

Operations Research (Mathematical Optimization)

Quadratic Assignment Problem

Computational Topology and Graph Theory

Computer Programming (Python/C#)