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Generative Design Research Methodology
Dr. Ir. Pirouz Nourian
Assistant Professor of Design Informatics
Department of Architectural Engineering & Technology
Faculty of Architecture and Built Environment
PhD in Design Informatics, MSc in Architecture, BSc in Electrical Engineering (Major in Control Systems Engineering)
Theoretical underpinnings of practice for systematic deduction and exploration in design
1
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Theme: Computational Design/Generative Design
[Mathematical] Design Optimization
Computational Topology Optimization
for Designing Compression-Only
Masonry/Earthy Buildings
Background and aim:
Masonry/Earthy structures can be made with sustainable
materials while offering strong structural properties,
especially if designed as compression-only structures.
Optimal masonry structures might take complex geometric
shapes that present architectural design and structural
validation challenges. We aim to develop computational
methods & tools for automatic generation of valid structural
forms.
Research question:
How to computationally generate valid designs for
compression-only brick/masonry structures?
Design objective:
To design and prototype a topology optimization tool for
designing masonry structures.
Methods:
•Topology Optimization (req. Calculus, Linear Algebra, etc.)
•Computational Topology & Geometry
•Finite Element Method
•Computer Programming (Python)
Image Credits: Rick van Dijk, thesis project, 2019-2020
Image Credits: Idil Gumruk, thesis project, 2017-2018
Image Credits: Karim Daw, Shervin Azadi, Pirouz Nourian, Hans Hoogenboom
Image Credits: Ivan Avdic, thesis project, 2018-2019
Latest Version: https://genesis-lab.dev/topics/computational-topology-optimization/ 2
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Mathematical] Design Optimization
3
Computational Shape Optimization
for Designing Compression-Only
Masonry/Earthy Buildings
Background and aim:
Masonry/Earthy structures can be made with sustainable
materials while offering strong structural properties,
especially if designed as compression-only structures.
Optimal masonry structures might take complex geometric
shapes that present computational design and structural
validation challenges. We aim to derive optimal shapes
directly based on a gradient descent optimization procedure
as a constructible structure.
Research question:
How to computationally find optimal catenary forms and
approximate them with modular brick/block structures?
Design objective:
To design and prototype a computational finite element
modeler for masonry structures.
Methods:
•Shape Optimization (req. Calculus, Linear Algebra, etc.)
•Computational Geometry & Topology
•Finite Element Analysis
•Computer Programming (Python or C#) Image Credits: Karim Daw, Shervin Azadi, Pirouz Nourian, Hans Hoogenboom
Image Credits (EARTHY 2019-2020): Yarai Zenteno, Fahriba Mustafa, Patrattakorn
Wannasawang, Akash Changlani, Elisa Vintimilla, Shasan Choksi
Latest Version: https://genesis-lab.dev/topics/computational-shape-optimization/
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Grammatical] Design Customization
Topological Shell Design
For Modular Approximation of Optimal
Catenary Shells
Image Credits (EARTHY 2019-2020):
Nikoleta Sidiropoulou, Hans Gamerschlag, Noah van den Berg,
Hamidreza Shahriari, Rick van Dijk, Maximilian Mandat
Image Credits: Karim Daw, Shervin Azadi, Pirouz Nourian, Hans Hoogenboom
Background and aim:
Masonry/Earthy structures can be made with sustainable
materials while offering strong structural properties,
especially if designed as compression-only structures.
Optimal masonry structures might take complex geometric
shapes that present constructability challenges. We aim to
develop combinatorial methods for approximating optimal
shells with modular blocks.
Research question:
How to approximate optimal shell structures as
an assembly of modular blocks for easy construction?
Design objective:
To design and prototype a combinatorial process
for modular polyhedral shape approximation.
Methods:
•Geometric Design and Tessellation
•Computational Geometry (optionally in Python)
•Structural Design and Mechanics
•Material Science
Latest Version: https://genesis-lab.dev/topics/topological-shell-design/ 4
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Grammatical] Design Customization
Combinatorial Surface & Solid Design
Polygonization and Polyhedralization
Background and aim:
In digitalization and industrialization of housing production, it
is a challenge is to provide affordable quality solutions at
scale. Modularity of building products can provide for
efficient production as well as diversification of the building
stock by means of combinatorial variations. We aim to
develop methods in computational geometry for mass-
customization of housing design-build processes based on
standardised architectural modules.
Research question:
How to design the Stereotomy of a limited set of blocks to
provide a large combinatorial surface/solid design space?
Design objective:
To design and prototype a computational and participatory
process for modular polygonal/polyhedral shape design.
Methods:
•Geometric Design and Tessellation
•Topological Polygonization & Polyhedralization
•Iso-surface Algorithms (Python or C#)
•Automatically writing Open-Standard Data Models
Image Credits: Marian42 and Oskar Stalberg
Image Credits: Hugo van Rossum,, Maren Hengelmolen, Liva Sadowska, Sander
Bentvelsen, Spatial Computing Architectural Design Studio - 2020 - CUB3D
Latest Version: https://genesis-lab.dev/topics/combinatorial-surface-solid-design/ 5
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Gamified] Participatory Design
Gamification of Generative Design
For Combinatorial Generation of
Modular Designs
Background and aim:
Participatory design is a necessity especially when multiple
stakeholders are to be involved in design and operation of
a building. Gamification of design into a combinatorial
configuration process provides an interactive medium for
stakeholders to be included in decision-making, explore the
vast possibilities of modular systems, and easily co-create
valid designs.
Research question:
How to formalize spatial design process as a participatory
game?
Design objective:
To design and prototype toolsets and tile-sets for
combinatorial design games aimed at modular assemblies.
Methods:
•Geometric Design and Tessellation
•Computational Topology & Geometry (optionally in Python)
•Gamification and Playful Learning
•Ergonomics
•Participatory Architectural Spatial Design
Image Credits: the Blookhood Game, Jose Sanches
Image Credits (EARTHY 2019-2020):
Alessandro Passoni, Alessio Vigorito, Fredy Fortich Kiana Mousavi, Stephanie Moumdjian
Image Credits: the Monument Valley Game
Latest Version: https://genesis-lab.dev/topics/gamification-of-generative-design/ 6
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Mathematical] Design Optimization
Generative Solar-Climatic Configuration
Mathematical Derivation of Building
Envelopes as to Climatic Requirements
Image Credits: Samaneh Rezvani, Pirouz Nourian
Image Credits: Shervin Azadi, project Go-Design
Image Credits (Spatial Computing, 2018-2019):
Fé van Lookeren Campagne, Max Ketelaar, Ruben Schonewille
Background and aim:
Early design decisions have great influence on final energy
performance and comfort of buildings. Proper consideration
of such influences requires using simulation engines. The
existing simulation methods can be too data demanding
and slow for early-stage computational design explorations
(e.g. in optimization loops or form-finding). The aim of this
project is to devise and prototype fast and design-friendly
simulation methods for generative design of optimal
envelopes.
Research question:
How to adapt and develop direct sunlight and view-shed
simulation methods for generation of optimal envelopes?
Design objective:
To design and prototype a suite of generative methods for
fair and optimal provision of solar irradiation, radiative
cooling potential and view shed.
Methods:
•Spatial Computing (Computational Geometry & Linear Algebra)
•Physical Analysis of Light and Principles of Naturally Lit Design
•Computer Programming (Python or C#)
Latest Version: https://genesis-lab.dev/topics/generative-solar-climatic-configuration/ 7
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Mathematical] Design Optimization
Latest Version: https://genesis-lab.dev/topics/generative-configuration-design/ 8
Background and aim:
Modeling the spatial design space discreetly will allow us to see
the configuration problem of a building as a discrete decision-
making problem. This will provide for ex-ante assessment of
various criteria that influence the functionality of a building in
terms of accessibility/visibility-related factors. The aim of the
research is to devise a systematic way of configuring
buildings in 3D to optimally meet functional requirements
pertaining to such factors etc.
Research question:
How to configure a building procedurally given a program of
requirements, accessibility, and daylight requirements and
complex site constraints?
Design objective:
To design and implement a computational 3D layout
methodology.
Methods:
•Architectural Engineering
•Multi-Criteria Decision Analysis
•Computational Topology and Graph Theory
•Computer Programming (Python/C#)
Generative Configuration Design
Feed-Forward 3D Configuring as to
Spatial Qualities and Requirements
Image Credits (Spatial Computing, 2018-2019):
Fé van Lookeren Campagne, Max Ketelaar, Ruben Schonewille,
optimal
design
Variant 3
Variant 1
Variant 2
Image Credits: Shervin Azadi, project Go-Design
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Mathematical] Design Optimization
Latest Version: https://genesis-lab.dev/topics/space-optimization/
Space Optimization
Spatial Re-Configuration through
Mathematical Optimization
Image Credits: Spatial Computing Architectural Design Studio - 2020 - CUB3D
Image Credits: Facility Management Network (link)
Image Credits: Spatial Computing Architectural Design Studio - 2020 - Green Valley
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#)
9
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Mathematical] Design Optimization
Latest Version: https://genesis-lab.dev/topics/ai-configurators/
AI Configurators
3D Layout via Reinforcement Learning
Image Credits: Pedro Veloso, Ramesh Krishnamurti, An Academy of Spatial Agents
Image Credits: Shervin Azadi, Pirouz Nourian, GoDesign
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#)
10
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Mathematical] Design Optimization
Background and aim:
Early design decisions have great influence on final energy
performance and comfort of buildings. The costs of
installation, maintenance, and the eventual replacement of
solar panels cannot be ignored in the financial outlook of a
building. This project aims to develop procedures for
including such financial considerations in addition to
architectural factors in optimal configuration of solar panels.
Research question:
How to configure an optimal array of photovoltaic cells on a
building envelope so as to maximize solar electricity yield
subject to architectural and economic constraints?
Design objective:
To computationally configure an optimal array of photovoltaics
on a building envelope.
Methods:
•Spatial Computing (Computational Geometry & Linear Algebra)
•Ladybug Recipes (environmental simulation workflows)
•Computer Programming (Python)
Maximizing Solar Electricity Yield
Computational Derivation of Configuration
of Photovoltaic Panel Arrays on Buildings
Image Credits: Tolga Ozdemir, MSc Thesis Project, 2019-2020
Latest Version: https://genesis-lab.dev/topics/maximizing-solar-electricity-yield/ 11
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Gamified] Participatory Design
Latest Version: https://genesis-lab.dev/topics/open-source-digital-twinning/
Open-Source Digital Twinning
Web-based Platforms for Spatial
Decision Support Systems
Image Credits: Aditya Soman & Shervin Azadi,
Image Credits: Brainport Development
Background and aim:
The decisions made on the spatial configurations and
distributions in the built environments (urban or rural
developments) have enormous impacts on the
environmental, economic, and societal sustainability of the
planet. The aim of the project is to provide mechanisms for
modelling the impacts of such spatial decisions using open-
source libraries and geo-spatial data to simulate complex
what-if scenarios for transparent and participatory decision-
making.
Research question:
How to make digital twins for spatial decision support using
open-data and open-source libraries?
Design objective:
To design and implement a computational framework for geo-
spatial digital twinning geared towards decision analysis.
Methods:
•geo-spatial computing (geo-informatics)
•geo-spatial analysis (spatial queries)
•geo-spatial Database Management Systems (DBMS)
•Computer Programming (Python/C#)
Image Credits: Brainport Development
Image Credits: Maartje Damen & Shervin Azadi
12
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Gamified] Participatory Design
Latest Version: https://genesis-lab.dev/topics/participatory-design-in-digital-twins/
Participatory Design in Digital Twins
Web-based Platforms for Participatory
Spatial Decision Making
Image Credits: Brainport Development
Image Credits: Shervin Azadi, EquiCity Game
Image Credits: Aditya Pravin Soman
Background and aim:
The decisions made on the spatial configurations and
distributions in the built environments (urban or rural
developments) directly influences the life of multiple
stakeholders. The aim of the project is to provide
mechanisms for involving different stakeholders in the spatial
decision making process and facilitating their negotiations
through Spatial Decision Support Systems (SDSS).
Research question:
How to make digital twins for participatory spatial decision
making?
Design objective:
To design and prototype a Multi-Actor and Multi-Criteria Spatial
Decision Support System.
Methods:
•geo-spatial computing (geo-informatics)
•geo-spatial analysis (spatial queries)
•geo-spatial Database Management Systems (DBMS)
•Computer Programming (Python/C#)
13
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Gamified] Participatory Design
Latest Version: https://genesis-lab.dev/topics/human-space-interaction-modelling/
Background and aim:
Humans and their environment have a reciprocal relation: we
constantly build and change our environment and our
environment shapes the way we use the space and navigate
through it. However, we still lack a comprehensive
understanding of how different spatial qualities affect legibility
of space and its ergonomy.
Research question:
How can we objectively assess the cognitive and ergonomic
comfort of a spatial configuration?
Design objective:
To design and develop a computational framework for
assessing the cognitive/ergonomic spatial qualities.
Methods:
•Spatial Computing (Computational Geometry & Linear Algebra)
•Mathematical Analysis of Visibility
•Deep Neural Networks (DNN, Artificial Intelligence)
•Computer Programming (Python or C#)
Human-Space Interaction Modelling
Spatial Cognition and Space Navigation
Image Credits: Z. Wang et al., 2019, doi: 10.1016/j.buildenv.2019.04.035.
14
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Mathematical] Design Optimization
Latest Version: https://genesis-lab.dev/authors/anastasia-florou/ 15
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Grammatical] Design Customization
Latest Version: https://genesis-lab.dev/authors/selina-bitting/ 16
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Gamified] Participatory Design
Latest Version: https://genesis-lab.dev/authors/aditya-soman/ 17
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
[Gamified] Participatory Design
Latest Version: https://genesis-lab.dev/authors/beza-bekele/ 18
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Generative Design
Generative Design
:=
{Simulation-Driven, Gradual, Transparent, Explainable, and Reproduceable}
‘Combinatorial Generation’
of
{a Navigable Catalogue of Valid/Optimal Design Alternatives}
Image Credit: Genesis Laboratory of Generative Systems and Sciences 19
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Relevance of Generative Design
The Scientific Method is
a meta-level term to describe research methods that can derive objectively verifiable and reproducible
knowledge. This lecture discusses the aim, the theoretical underpinnings, and the practice, of the scientific
method in systematic exploration/itemization and/or systematic deduction/derivation in architectural
design given measurable functional or performance objectives and physical constraints.
ethos
Open-Science, Mathematical Transparency, Scientific Discovery, Participatory Decision-Making,
Explainable Artificial Intelligence, Modularity, Reproducibility, Critical Thinking, Intellectual Freedom
raisons d'être
Efficacy, Equity, Sustainability, Circularity, Quality of Life (w.r.t. Ergonomics & Human Factors)
20
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Why Generative Design?
Architectural Engineering
∋
{Engineering, Human Factors, and Ergonomics}
Image Credit: GoDesign Generative Design Framework 21
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Why Generative Design?
Image Credit: Genesis Laboratory of Generative Systems and Sciences 22
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Methodology?
Research Methodology >σ𝐢𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐌𝐞𝐭𝐡𝐨𝐝𝐢
23
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Design Science?
Research MethodologyDesign ResearchGenerative Design
Design Science
Generative Design Research Methodology
24
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Definitions
Generative Design≔Systematic (methodical) processes for synthesis of plans, configurations and forms
Generative Design Methods, Techniques, and Products
Grammatical Methods
Mathematical Methods
Systematic Processes
Networks
Graphs
Plans
Layouts
Topologies
Configurations
Shapes
Geometries
Forms
Serious Games
Synthesis
Exploration/Itemization
Deduction/Derivation
for of
,
,
, , and
, and
, and
METHODS TECHNIQUES PRODUCTS
25
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Definitions
Generative Design≔Systematic (methodical) processes for synthesis of plans, configurations and forms
Generative Design Methods, Techniques, and Products
Grammatical Methods
Mathematical Methods
Systematic Processes
Networks
Graphs
Plans
Layouts
Topologies
Configurations
Shapes
Geometries
Forms
Serious Games
Synthesis
Exploration/Itemization
Deduction/Derivation
for of
,
,
, , and
, and
, and
METHODS TECHNIQUES PRODUCTS
Design
Engineering
Mathematics
abstract concrete
26
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Definitions
Computational Design Paradigms
•Dorst, Kees. "On the problem of design problems-problem solving and design expertise." Journal of design research 4.2 (2004): 185-196.
•Lawson, Bryan. "Schemata, gambits and precedent: some factors in design expertise." Design studies 25.5 (2004): 443-457.
•Simon, Herbert A. The sciences of the artificial. MIT press, 2019.
•Schön, D. (1938). The reflective practitioner. New York, 1983.
•Nourian, P. “Configraphics: Graph Theoretical Methods for Design and Analysis of Spatial Configurations,” Doi.Org, vol. 6, no. 14. pp. 1–348, 2016.
•Nourian, P., How to write a thesis? A Generative Design Graduate Studio Guidebook. (2019). Delft University of Technology, Faculty of Architecture and Built Environment, Department
of Architectural Engineering and Technology. DOI: 10.6084/m9.figshare.11987328
•S. Azadi and P. Nourian, “Collective Intelligence in Generative Design: A Human-Centric Approach Towards Scientific Design,” BouT: Periodical for the Building Technologist, vol. Generative Design, no. 76,
pp. 7–16, Apr. 2021. doi: 10.13140/RG.2.2.15295.84642. [URL]
•S. Azadi and P. Nourian “GoDesign - A modular generative design framework for mass-customization and optimization in architectural design,” 2021. Accessed: Aug. 16, 2021. [Online]. Available:
http://papers.cumincad.org/cgi-bin/works/paper/ecaade2021_263 [URL]
•P. Nourian, S. Azadi, H. Hoogenboom, S. Sariyildiz, Earthy: Generative Design for Earth and Masonry Architecture, DOI: 10.13140/RG.2.2.28390.65607, [URL]
“Design as a Reflective Practice” (Schon, 1983) “Design as Rational Problem Solving” (Simon, 1996)
Procedural Design
Computational Design
Creative Design Generative Design
Parametric Design Combinatorial Design
Continuous Variations Discrete Variations
Inherently Analog Inherently Digital
Feed-back Optimization Feed-forward Optimization
Real Parameters (ℝ3)Integer Parameters (ℤ3)
Geometric Variation/Evolution Topological Variation/Evolution
Integrated Products Modular Products
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Mathematical Derivation
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References
Scientific Paradigms
More information: https://www.thwink.org/sustain/glossary/KuhnCycle.htm
The Structure of Scientific Revolutions, Thomas Kuhn
Image credit: http://scienceprogress.org/wp-content/uploads/2013/01/DuckRabbit_full.gif
Loosely speaking,
Scientific Paradigms≃Scientific Worldviews
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References
Co-Existing Scientific Paradigms
Rabbit
Duck
e.g. in humanities
behaviourism in psychology versus cognitivism in psychology
Loosely speaking,
Scientific Paradigms≃Scientific Worldviews
More information: https://www.thwink.org/sustain/glossary/KuhnCycle.htm
The Structure of Scientific Revolutions, Thomas Kuhn
Image credit: http://scienceprogress.org/wp-content/uploads/2013/01/DuckRabbit_full.gif
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References
Scientific Revolutions
Aristotelian, Newtonian, Einsteinian
More information: https://www.thwink.org/sustain/glossary/KuhnCycle.htm
The Structure of Scientific Revolutions, Thomas Kuhn
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References
Scientific Revolutions
Aristotelian, Newtonian, Einsteinian
Methodologically
exemplified in both Macro-
scale theories of Albert
Einstein (relativity) and
Micro-scale theories of
Niels Bohr (quantum
physics). Although
competing for establishing
‘a theory of everything’,
they both have a similar
theoretical approach:
Mathematics and
Computation are the
drivers and the methods
of discovery. Models are
synthesized to
investigate complexity.
Theoretical Physics is the
best example.
Based on the writings of
Ptolemy, Aristotle, Plato:
Science was considered
as a branch of theology,
assuming a duality of
‘ideal/divine forms’ and
the material world. Earth
was the centre of a static
universe. Medieval Times.
Started with the Scientific Revolution,
pioneered by Nicolaus Copernicus,
Galileo Galilei, and flourished in the
Renaissance, culminated in the works
of Isaac Newton and Rene Descartes:
Science is defined as a methodical
practice in pursuit of objective
explanations and justifications for
observations. Earth is a planet in a
material universe. Renaissance &
Enlightenment Era: still dominant in
earthy applications.
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Definitions
Design Research can be described as the field of study dealing with (Cross, 1999):
•Design Epistemology: that is about studying [processes of acquisition of] design knowledge and ‘how
designers think’*(Lawson, 1980) and the nature of their so-called tacit knowledge, knowhow, and
Experience.
•Design Praxeology: that is about how designers work in practice, how the design process proceeds
and what is/could be the role of design, representations, tools, and media in designing
•Design Phenomenology: that is about studying the nature of what is produced in designing, mainly
about form and configuration studies (conventionally known as architectural or urban morphology)
*Design Epistemology in Generative Design is mostly concerned with physics-based mathematical notions of
functionality or performance rather than the social ways of learning the so-called tacit knowledge of design.
Cross, N., 1999. Design Research as a Disciplined Conversation. Design Issues, 15(2), pp. 5-10.
Lawson, B., 1980. How designers think. 4th, 2005 ed. Burlington: Architectural Press, Elsevier.
Horvath, I., 2004. A treatise on order in engineering design research. Research in Engineering Design, 15(3), pp. 155-181.
Nourian, P. 2016, Configraphics: Graph Theoretical Methods for Design and Analysis of Spatial Configurations,, vol. 6, no. 14. pp. 1–348,
2016.
Design Research
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Definitions
“The term methodology refers to ‘a structured collection of methods’, in the same way that the
term technology means ‘a structured collection of techniques’. What distinguishes a research
methodology from a mere collection of methods is a set of theoretical underpinnings, which
can be considered as ‘the theory of the methods’ (Horvath, 2004).”(Nourian, 2016)
Research Methodology
Horvath, I., 2004. A treatise on order in engineering design research. Research in Engineering Design, 15(3), pp. 155-181.
Nourian, P. 2016, Configraphics: Graph Theoretical Methods for Design and Analysis of Spatial Configurations,, vol. 6, no. 14. pp. 1–348, 2016.
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References
Definitions
Design Methodology
Kroes, P. "Design methodology and the nature of technical artefacts." Design studies 23.3 (2002): 287-302.
Horvath, I., 2004. A treatise on order in engineering design research. Research in Engineering Design, 15(3), pp. 155-181.
Nourian, P. 2016, Configraphics: Graph Theoretical Methods for Design and Analysis of Spatial Configurations,, vol. 6, no. 14. pp. 1–348, 2016.
“the logical leap in design”an abstract function a concrete form
“This is an important distinction: not every design technology can be regarded as a design methodology.
Many design tools can be used in various situations without enforcing or suggesting a particular way of
designing, i.e. a method for avoiding “the logical leap in design” (Kroes, 2002)” (Nourian, 2016)
“The term methodology refers to ‘a structured collection of methods’, in the same way that the
term technology means ‘a structured collection of techniques’. What distinguishes a research
methodology from a mere collection of methods is a set of theoretical underpinnings, which
can be considered as ‘the theory of the methods’ (Horvath, 2004).”(Nourian, 2016)
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Definitions
Design Methodology
Feedforward Generative Design
“This is an important distinction: not every design technology can be regarded as a design methodology.
Many design tools can be used in various situations without enforcing or suggesting a particular way of
designing, i.e. a method for avoiding “the logical leap in design” (Kroes, 2002)” (Nourian, 2016)
an abstract function a concrete form
“The term methodology refers to ‘a structured collection of methods’, in the same way that the
term technology means ‘a structured collection of techniques’. What distinguishes a research
methodology from a mere collection of methods is a set of theoretical underpinnings, which
can be considered as ‘the theory of the methods’ (Horvath, 2004).”(Nourian, 2016)
Kroes, P. "Design methodology and the nature of technical artefacts." Design studies 23.3 (2002): 287-302.
Horvath, I., 2004. A treatise on order in engineering design research. Research in Engineering Design, 15(3), pp. 155-181.
Nourian, P. 2016, Configraphics: Graph Theoretical Methods for Design and Analysis of Spatial Configurations,, vol. 6, no. 14. pp. 1–348, 2016.
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References
The Sciences of the Natural The Sciences of the Artificial
Physical Sciences and Life Sciences (Physics, Chemistry, Biology,
etcetera)
Design Sciences, Engineering [Optimization], and Operations
Research (Planning, Scheduling, Management)
concerned with the description, prediction, and understanding
of natural phenomena, based on empirical
evidence from observation and experimentation
concerned with reasoning, envisioning, and devising artificial
systems based on given design requirements or optimization
objectives
Questions Problems
Theories as proven Hypotheses Methods as approved Propositions
Explanation and Justification Transformation and Reasoning
Deterministic Models, Stochastic Models Configured Systems, Reconfigurable Systems
Mathematical and/or Computational
Modelling, Analysis, & Simulation
Mathematical and/or Computational
Problem Formulation & Problem Solving
How do things change? How to change things?
MATHEMATICS
“mathematics is the queen of sciences, [and the main driving force behind scientific discovery]”
Carl Friedrich Gauss, [additions from Marcus du Sautoy]
Mathematics is not exactly a branch of science but rather the universal language of sciences.
It does not concern itself with concrete matters but with abstract patterns behind them.
More Information: https://en.wikipedia.org/wiki/The_Sciences_of_the_Artificial
Definitions
Generative Design Research ≔The Sciences of the Artificial
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References
Causal Systems vs. Conscious Beings
https://en.wikipedia.org/wiki/Anticipatory_Systems;_Philosophical,_Mathematical,_and_Methodological_Foundations
https://www.springer.com/gp/book/9781461412687
There is no reason for water boiling, there is
a cause for it. Water does not decide to boil,
it just boils, for a cause, prior to the effect.
The boss is angry for a reason (a valid reason?). The
boss decides what to do next, based on an anticipation
of future, posterior to the action.
The behaviour of causal systems is inherently predictable, whereas the behaviour of conscious beings seems to be [is?]
unpredictable.
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The Scientific Method
“A method of procedure that has characterized natural sciences since the 17th century,
consisting in systematic observation, measurement, and experiment, and the formulation, testing,
and modification of hypotheses.”
‘criticism [scepticism] is the backbone of the scientific method’
from Oxford Dictionary
Also known as:
Mathematical Modelling
And since ~1970’s also as:
Computational Modelling/Simulation
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The Scientific Method≠Statistcis
Hypothesis
Testing
Data Analysis
Observation
Question
Mathematical
Modelling
Computational
[Simulation] Modelling
“A method of procedure that has characterized natural sciences since the 17th century,
consisting in systematic observation, measurement, and experiment, and the formulation, testing,
and modification of hypotheses.”
‘criticism [scepticism] is the backbone of the scientific method’
from Oxford Dictionary
Also known as:
Mathematical Modelling
And since ~1970’s also as:
Computational Modelling/Simulation
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What is a model?
Physical Model
Statistical Model
Phillip's Economic Computer
Mathematical
Model
𝑇𝑖,𝑗 = 𝐺 𝑚𝑖𝑚𝑗
𝐷𝑖,𝑗
2
Gravity Model of Trade
A Markov Chain Model
of the Stock Market
Computational Model
𝒙(𝑡+1) = 𝑷𝒙(𝑡)
Fitting a Linear Model to Data Points
A simplified replica of a system that is capable of partially mimicking the behaviour of the system.
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Why model?
We make a model to perceive/grasp/understand a phenomenon or a system.
Structure/Mechanism: A good/useful model not only helps predict some output of a system
but also helps us understand and explain the structures/mechanisms sophisticating the
behaviour of a system/phenomenon.
Dynamics: We often make models to study how a change in the conditions results in a change
in the behaviour of a system. We model systems and phenomena in terms of variable inputs
and dynamic outputs.
Didactics: The process of learning or grasping something can be deemed as a process of
evolving mental models, from overly simplistic ones to more sophisticated ones.
A simplified replica of a system that is capable of partially mimicking the behaviour of the system.
Epstein, Joshua M. "Why model?." Journal of Artificial Societies and Social Simulation 11.4 (2008): 12. 41
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References
Professor Butts and the Self-Operating Napkin (1931), a cartoon by Rube Goldberg
Image Credit: https://en.wikipedia.org/wiki/Rube_Goldberg_machine A Facebook® creation designed for making life more or less complicated
COMPLEX vs COMPLICATED
Complexity
complex≠complicated
Complicated is that which is difficult to grasp or deal with.
Complex is that which is sophisticated in that it [spatially] consists of many parts interrelated to one another, [temporally] demonstrates circular causality
because of feedback loops, and/or combinations of both sophistications resulting in emergent patterns and/or chaotic behaviour.
Complexity Sciences study real-world complex phenomena by making simplified mathematical/computational replicas. 42
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Collective Intelligence: Complexity & Emergence
https://en.wikipedia.org/wiki/Emergence
Organization and Structure
Emerging out of Collective
Behaviour
The whole being greater than the
sum of parts, having [smart]
properties arising from interactions
of the [not-necessarily smart] parts.
Irreducibility: the system can only
be understood by looking at the
interactions of the parts. The system
cannot be reduced to [some of] its
constituent elements.
A Termites' Cathedral manifesting the genesis of a magnificent masonry architecture through collective
intelligence, Photographed by Fiona Stewart in Queensland, featured by Prof. Richard Dawkins,
Comparison with Sagrada Familia of Antoni Gaudi by the Philosopher Daniel Dennett in his book From
Bacteria to Bach and Back: The Evolution of Minds. Prof. Richard Dawkins: No architect (as Dan Dennett
pointed out) no blueprint, not even in DNA. They just followed local rules of thumb, like cells in an
embryo. ref
More Information: https://genesis-lab.dev/courses/earthy/ 43
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Collective Intelligence: Complexity & Emergence
Organization and Structure Emerging out of Collective Behaviour
The whole being greater than the sum of parts, having [smart] properties arising from interactions of
the [not-necessarily smart] parts. Irreducibility: the system can only be understood by looking at the
interactions of the parts. The system cannot be reduced to [some of] its constituent elements.
A slime mold network foraging network replicating the design of the metropolitan rail network of metropolitan area of Tokyo. The genesis of a highly sophisticated network
through collective intelligence is a source of inspiration for Generative Design of Architectural Configurations by devising Multi-Agent Systems in the course Spatial Computing
Design Studio. Image from AAAS/Science
More Information: https://genesis-lab.dev/courses/spatialcomputing/ 44
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Definitions
Leading AI textbooks define the field as the study of "intelligent agents": any device that
perceives its environment and takes actions that maximize its chance of successfully achieving
its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or
computers) that mimic "cognitive" functions that humans associate with the human mind, such
as "learning" and "problem solving".[4]
Artificial Intelligence=
“Machine’s ability to discern, map, and decide in face of complexity and uncertainty”
1. Definition of AI as the study of intelligent agents: Poole, Mackworth & Goebel (1998), which provides the version that is used in this article.
These authors use the term "computational intelligence" as a synonym for artificial intelligence.[1]
2.Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field".[2]
3.Nilsson 1998
4.Legg & Hutter 2007
5.Russell & Norvig 2009, p. 2.
Explainable AI (XAI)=“methods and techniques in the application of artificial
intelligence technology (AI) such that the results of the solution can be understood by humans”
(https://en.wikipedia.org/wiki/Explainable_artificial_intelligence)
Machine Learning, Machine Vision, Fuzzy Logics, Multi-Agent Systems, Meta-Heuristics,
etcetera.
“Post-Modern Mathematics”
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Definitions
There are two major dimensions along which research methodologies differ with respect to
their underlying approaches, epistemological principles, and praxis: rationalism-empiricism
and atomism-holism. The distinctions are made to help with navigation, the world of research
methods is more connected than what may appear in this image.
Research Methodology
https://science-network.tv/philosophy-of-science/ 46
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Definitions
There are two major dimensions along which research methodologies differ with respect to
their underlying approaches, epistemological principles, and praxis: rationalism-empiricism
and atomism-holism. The distinctions are made to help with navigation, the world of research
methods is more connected than what may appear in this image.
Research Methodology
https://science-network.tv/philosophy-of-science/
Generative Design
Research Methodology
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Definitions
Research Methodology
Objectivity Inter-Subjectivity Subjectivity
Context of Justification Context of Interpretation Context of Discovery
Anybody and Everybody The Community The Individual
The Universal Realm The Social Realm The Private Realm
Facts Social Constructs Opinions
Repeatable Experiment Social Consensus Personal Experience
Complexity Sciences
The Spectrum of Epistemology
•Okasha, S., 2002. Philosophy of Science: A Very Short Introduction. s.l.: Oxford University Press, USA.
•Fallman, D., 2003. Design-oriented Human—Computer Interaction. s.l., ACM, pp. 225-232.
•Dorst, K., 2007. The Problem of the Design Problem. In: Expertise in Design - Design Thinking Research Symposium 6. Sydney: Creativity and Cognition Studios Press.
“Not everything that counts can be counted, and not everything that can be counted counts.”
(William Bruce Cameron)
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Logic (from Greek: λόγος (lógos,“speech, reason”) or λογική,logikḗ,
'possessed of reason,intellectual,dialectical,argumentative')[1][2][i] is the systematic study of valid rules of inference, i.e.
the relations that lead to the acceptance of one proposition (the conclusion) on the basis of a set of other propositions
(premises).
Definitions
Method: from Greek words μετά+οδος or μέθοδος, that can be translated to ‘way of ways’.
“A particular procedure for accomplishing or approaching something, especially a systematic or established one.”
Oxford Dictionary
The Scientific Method is a meta-level term to describe research methods that can derive objectively
verifiable and reproducible knowledge, while ensuring rigorous scepticism and rejecting bias.
Research Methodology
Praxis
praxis, noun /ˈpræksɪs/ [uncountable] (philosophy)
a way of doing something; the use of a theory or a belief in a practical way
“Analysis, Synthesis, and Evaluation by means of Logical Reasoning, refuting Logical Fallacies”
λόγος≃“The art (grammar) and the science (mathematical logic) of reasoning”⊂Trivium
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Definitions
Research Methodology
Generative Design Praxis
praxis, noun /ˈpræksɪs/ [uncountable] (philosophy)
a way of doing something; the use of a theory or a belief in a practical way
Linguistics: Generative Grammars (Grammatical/Syntactic Exploration or Itemization),
Partial Differential Equations: Mathematical Optimization (Mathematical/Physical Deduction or Derivation)
Graph Theory + Game Theory: Serious Gaming for systematic Negotiation and Consensus Building
λόγος (lógos,“speech, reason”)
Analysis, Synthesis, and Evaluation by means of Logical Reasoning, refuting Logical Fallacies
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Linguistics: Generative Grammars
https://elrincondeniko.wordpress.com/2016/08/21/linguistics-for-dummies-transformational-generative-grammar-i/
(Grammatical/Syntactic Exploration or Itemization)
Image credit: https://en.ppt-online.org/286759
Noam Chomsky, Photo credit: Jimi Giannatti
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Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 52
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Epistemology
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Mathematical Derivation
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References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 53
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Research Compass
Epistemology
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Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 54
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References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 55
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 56
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 57
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 58
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 59
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 60
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 61
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 62
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 63
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 64
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 65
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 66
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Linguistics: Generative Grammars
(Grammatical/Syntactic Exploration or Itemization)
Image Credit: Tarang Gupta, Divyaye Mittal, Prateek Wahi, Andrea Fumagalli, Filip Zielinski, Aditya Parulekar, EARTHY 2019 67
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Mathematics: Topology Optimization
(Mathematical Derivation of form from Physical Laws [Partial Differential Equations])
Image Credit: Ir. Rick van Dijk, Graduation thesis project, https://genesis-lab.dev/graduation/ 68
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Mathematics: Topology Optimization
(Mathematical Derivation of form from Physical Laws [Partial Differential Equations])
Image Credit: Ir. Rick van Dijk, Graduation thesis project, https://genesis-lab.dev/graduation/ 69
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Mathematics: Topology Optimization
(Mathematical Derivation of form from Physical Laws [Partial Differential Equations])
Image Credit: Ir. Rick van Dijk, Graduation thesis project, https://genesis-lab.dev/graduation/ 70
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Mathematics: Topology Optimization
(Mathematical Derivation of form from Physical Laws [Partial Differential Equations])
Image Credit: Ir. Rick van Dijk, Graduation thesis project, https://genesis-lab.dev/graduation/ 71
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Games: Linguistics & Mathematics
Systematic Rule-Based/Syntactic Exploration
Image Credit: Project TerraTetris, Aditya Pravin Soman, Vicente Blanes, Christina Koukelli, Neha Gupta, and Dion van Vlarken, Earthy 3.0
Spatial Decision Making, (EquiCity Game, 2021)
72
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Games: Linguistics & Mathematics
Systematic Rule-Based/Syntactic Exploration
Image Credit: Project TerraTetris, Aditya Pravin Soman, Vicente Blanes, Christina Koukelli, Neha Gupta, and Dion van Vlarken, Earthy 3.0 73
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Games: Linguistics & Mathematics
Systematic Rule-Based/Syntactic Exploration
Image Credit: Project TerraTetris, Aditya Pravin Soman, Vicente Blanes, Christina Koukelli, Neha Gupta, and Dion van Vlarken, Earthy 3.0 74
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Games: Linguistics & Mathematics
Systematic Rule-Based/Syntactic Exploration
Image Credit: Alessandro Passoni, Alessio Rigorito, Kiana Mousavi, Fredy Fortich Mora, Stephanie Moumdjian, EARTHY 2.0 75
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Games: Linguistics & Mathematics
Systematic Rule-Based/Syntactic Exploration
Image Credit: Alessandro Passoni, Alessio Rigorito, Kiana Mousavi, Fredy Fortich Mora, Stephanie Moumdjian, EARTHY 2.0 76
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Games: Linguistics & Mathematics
Systematic Rule-Based/Syntactic Exploration
Image Credit: Alessandro Passoni, Alessio Rigorito, Kiana Mousavi, Fredy Fortich Mora, Stephanie Moumdjian, EARTHY 2.0 77
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Games: Linguistics & Mathematics
Systematic Rule-Based/Syntactic Exploration
Image Credit: Alessandro Passoni, Alessio Rigorito, Kiana Mousavi, Fredy Fortich Mora, Stephanie Moumdjian, EARTHY 2.0 78
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Games: Linguistics & Mathematics
Systematic Rule-Based/Syntactic Exploration
Image Credit: Alessandro Passoni, Alessio Rigorito, Kiana Mousavi, Fredy Fortich Mora, Stephanie Moumdjian, EARTHY 2.0 79
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Games: Linguistics & Mathematics
Systematic Rule-Based/Syntactic Exploration
Image Credit: Anne Leltz, student work in BK3OV3, an example of Topological Design via the Walk-Stand-Sit Method, 2017-2018, tutor, Pirouz Nourian
Compose a Program of Requirements (PoR):
●A list of Functional Spaces and their sizes and
desirable proportions and free-heights.
●A matrix of relations among these spaces, as a Activity
Relations Chart (ARC)
Define three layers of spaces for walking, standing, and sitting
For every functional space (where people are to sit) make a
sized and proportioned rectangle.
Make modularized ramps, stairs and corridor pieces.
Connect the functional (sitting space) rectangles using ramps,
stairs, and corridors.
Consider a bounding box smaller than or equal to the available
volume and fit all functional spaces + connections inside the
bounding box considering their free heights. This will be the
spatial configuration.
You can morph the spatial configuration topologically to fit to
the desired shape.
Walk-Stand-Sit: The 7 Golden Game Rules
80
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Mathematical Design Games
Image Credits: Maximillian Michl, Course Name, Spatial Computing
Noise Analysis
Connectivity
Sunlight Analysis 81
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Mathematical Design Games
Image Credits: Maximillian Michl, Course Name, Spatial Computing
Massing Synthesis
Pathways & Space Seeds
Configuration=Massing & Circulation 82
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Mathematical Design Games
Image Credits: Maximillian Michl, Course Name, Spatial Computing 83
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
84
MSc3, BT, AR3B011
Generative Design Methodology Projects
Decision Making Panel, (EquiCity Game, 2021)
Profile Panel, (EquiCity Game, 2021)Game Information Panel, (EquiCity Game, 2021)
Mathematical Game Engine Flowchart,
Pirouz Nourian & Shervin Azadi (EquiCity
Game, 2021)
Decision Making Panel, (EquiCity Game, 2021)
Profile Panel, (EquiCity Game,
2021)
Game Information Panel,
(EquiCity Game, 2021)
Decision Making Panel,
(EquiCity Game, 2021)
problem
dimensions
constraints
mathematical
methods
.allocation of discrete spatial units to different functions
.actors, sites, functions (colors)
.different assignment of colors to sites
.required area per color for the district
.limited area per site
.Online Interactive Interface (Multi-Actor)
.Opinion Pooling (Multi-Actor)
.Proportional Fitting (Multi-Actor)
.Weighted Massing (Multi-Dimensional)
.Spatial Performance Evaluation (Multi-Dimensional)
.Multi Criteria Decision Analysis (Multi-Criteria)
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
MSc3, BT, AR3B011
Generative Design Methodology Projects
Decision Making Panel, (EquiCity Game, 2021)
Profile Panel, (EquiCity Game, 2021)Game Information Panel, (EquiCity Game, 2021)
Decision Making Panel, (EquiCity Game, 2021)
Profile Panel, (EquiCity Game,
2021)
Game Information Panel,
(EquiCity Game, 2021)
Decision Making Panel,
(EquiCity Game, 2021)
.educational context (MSc and BSc)
.research context (EquiCity)
.topoGenesis (python library)
.supporting variety of methodologies from optimization to
gamification
.supporting variety of spatial quality criteria
.compatible with both modular and integral construction
techniques
.hard to reach specific solution because of the decision
priority
.formulation of spatial quality criteria
.benchmarking with standardized problems and methods
.connections with Reinforcement learning
application
implementation
generality
limitations
future works
Image Credits: Hugo van Rossum, Maren Hengelmolen. Liva Sadovska, Sander Bentvelsen, Spatial Computing Design Studio 3.0 85
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Generative Design:
•Spatial Mathematics (Linear Algebra, Computational Geometry, Topology, Graph Theory)
•Fundamentals of Computer Science
•Algorithm Design and Programming (Python, C#)
•Gamification of Design
•Modular & Combinatorial Design
Minor BK-MI-197
MSc3, BT, AR3B011
https://genesis-lab.dev/courses/spatialcomputing/ https://genesis-lab.dev/courses/earthy/
Generative Design Courses
86
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Image from: https://genesis-lab.dev/about/
Generative Design Tools
87
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
More info: https://genesis-lab.dev
Acknowledgements
88
Dr.ir. Matthijs Langelaar,
Associate Professor of
Structural Optimization and Mechanics
Scientific Advisor of Genesis Lab
Prof.dr. E. Eisemann,
Professor of
Computer Graphics & Visualization
Scientific Advisor of Genesis Lab
Ir. Shervin Azadi
Researcher and Instructor of
Design Informatics
Technological Director of Genesis Lab
Dr. Tom van Mele,
Co-director of Block Research Group
Computational Design & Engineering
Scientific Advisor of Genesis Lab
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Read more:
about Agent-Based Models, Cellular Automata and Markov Chains
and their use in modelling spatial and temporal dynamics of social phenomena
here.
Further reading:
[1] J. M. Epstein, “Why Model?,” J. Artif. Soc. Soc. Simul., vol. 11, no. 4, p. 12, 2008.
[2] T. Schelling, Micromotives and Macrobehavior. 2006.
[3] A. J. Heppenstall, A. T. Crooks, L. M. See, and M. Batty, Eds., Agent-based models of geographical systems, Heppenstal. Dordrecht: Springer, 2012.
[4] S. P. Borgatti and P. C. Foster, “The network paradigm in organizational research: A review and typology,” Journal of Management, vol. 29, no. 6. pp. 991–1013, 2003.
[5] P. Nourian, “Configraphics: Graph Theoretical Methods for Design and Analysis of Spatial Configurations,” Doi.Org, vol. 6, no. 14. pp. 1–348, 2016.
[6] G. P. Richardson, “System Dynamics,” in Encyclopaedia of Operations Research and Management Science, Boston, MA: Springer US, 2013, pp. 1519–1522.
[7] R. Oval, Topology Finding of Patterns for Structural Design, Universite Paris Est, PhD dissertation
[8] N. Bai, S. Azadi, P. Nourian, A. Pereira Roders, Decision-Making as a Social Choice Game: Gamifying an urban redevelopment process in search for consensus,
eCAADe2020 –Anthropologic, TU Berlin, Germany, Volume: 2
[9] P. Nourian, How to write a thesis? A Generative Design Graduate Studio Guidebook. (2019). Delft University of Technology, Faculty of Architecture and Built
Environment, Department of Architectural Engineering and Technology. DOI: 10.6084/m9.figshare.11987328
[10] P. Nourian, S. Azadi, and F. van Andel, “Open Source Participatory Design and Construction of Open Buildings: Affordable ‘Haute Couture’ for the Masses by means
of Design-Build Games:,” presented at the Open Building Now! 2.0, Mar. 16, 2021. doi: 10.13140/RG.2.2.30315.46888. [URL]
[11] P. Nourian, Generative Design Research Methodology: Theoretical underpinnings of practice for systematic deduction and exploration in design. 2020. doi:
10.13140/RG.2.2.30096.84484. [URL]
[12] S. Azadi and P. Nourian, “Collective Intelligence in Generative Design: A Human-Centric Approach Towards Scientific Design,” BouT: Periodical for the Building
Technologist, vol. Generative Design, no. 76, pp. 7–16, Apr. 2021. doi: 10.13140/RG.2.2.15295.84642. [URL]
[13] S. Azadi and P. Nourian “GoDesign - A modular generative design framework for mass-customization and optimization in architectural design,” 2021. Accessed: Aug.
16, 2021. [Online]. Available: http://papers.cumincad.org/cgi-bin/works/paper/ecaade2021_263 [URL]
[14] P. Nourian, S. Azadi, H. Hoogenboom, S. Sariyildiz, Earthy: Generative Design for Earth and Masonry Architecture, DOI: 10.13140/RG.2.2.28390.65607, [URL]
Further Reading
89
Motivation
Introduction
Relevance
Design Paradigms
Design Research
Methodology
D/R Methodology
About Science
The Scientific Method
Modelling
Complexity
Research Compass
Epistemology
Praxis
Generative Design
Grammatical Itemization
Mathematical Derivation
Gamified Exploration
References
Video Presentation
Questions:
p.nourian[]tudelft.nl
90
