Lecture notes of the course Future Models I (AR1TWF030), The Why Factory, Directed by Prof. Winy Mass, TU Delft, Faculty of Architecture and Built Environment
Lecture notes of the course Future Models I (AR1TWF030), The Why Factory, Directed by Prof. Winy Mass, TU Delft, Faculty of Architecture and Built Environment
This is a short presentation given in the context of a computational design course for MSc architectural engineering students. It is hopefully insightful for other engineering students as well.
Enhancing Innovation in STEM by Exploring Aesthetics Derek Ham
This presentation was presented at the 2nd Annual Bridging the Gap STEM Conference in Raleigh, NC. Discover how K-16 STEM curricula should readily embrace aesthetics as a core component of their pedagogy. By doing so, it opens a new world of creativity and innovation for STEM inquiry. We present a compelling argument for pulling aesthetics out of art education curricula to be placed right at the center of STEM education. This session was hands-on, allowing attendees to participate in learning concepts through an interactive educational game called SHAPE.
Prototyping is a great way of developing, communicating and validating design ideas and requirements in a quick and cost-effective manner, when devising a user experience.
This presentation discusses what prototypes are, why they are useful, the various tools that can be used and some basic principles to adopt.
This presentation was delivered by Stephen Denning as part of the User Vision Breakfast Briefing series in 2012.
Lecture notes of the course Future Models I (AR1TWF030), The Why Factory, Directed by Prof. Winy Mass, TU Delft, Faculty of Architecture and Built Environment
This is a short presentation given in the context of a computational design course for MSc architectural engineering students. It is hopefully insightful for other engineering students as well.
Enhancing Innovation in STEM by Exploring Aesthetics Derek Ham
This presentation was presented at the 2nd Annual Bridging the Gap STEM Conference in Raleigh, NC. Discover how K-16 STEM curricula should readily embrace aesthetics as a core component of their pedagogy. By doing so, it opens a new world of creativity and innovation for STEM inquiry. We present a compelling argument for pulling aesthetics out of art education curricula to be placed right at the center of STEM education. This session was hands-on, allowing attendees to participate in learning concepts through an interactive educational game called SHAPE.
Prototyping is a great way of developing, communicating and validating design ideas and requirements in a quick and cost-effective manner, when devising a user experience.
This presentation discusses what prototypes are, why they are useful, the various tools that can be used and some basic principles to adopt.
This presentation was delivered by Stephen Denning as part of the User Vision Breakfast Briefing series in 2012.
Taking portfolio benefits management to the next level with modern analytics webinar
Wednesday 13 June 2018
presented by Ian Stuart, Altis Consulting, Principal
hosted by Merv Wyeth, Benefits Management SIG Secretary
The link to the write up page and resources of this webinar:
https://www.apm.org.uk/news/taking-portfolio-benefits-management-to-the-next-level-with-modern-analytics-webinar/
This examines the potential for the application of Design Science principles to the solution design process within solution architecture to improve the rigour and accuracy of solution designs.
Design Science is the structured and systematic process for creating designs that resolve problems. It is concerned with the structured process for the acquisition and application of knowledge in relation to the problems to the resolved and the solution knowledge to be applied.
The application of Design Science must be a means to an end – better solution quality – and not an end in itself – an incentive for the design function is to become large.
Solution architecture requires a (changing) combination of technical, leadership, interpersonal skills, experience, analysis, appropriate creativity, reflection and intuition applied in a structured manner.
Knowledge management – problem knowledge and solution knowledge – is at the core of the application of design science principles.
Knowledge management requires good management of the solution architecture function.
Lean Analytics is a set of rules to make data science more streamlined and productive. It touches on many aspects of what a data scientist should be and how a data science project should be defined to be successful. During this presentation Richard will present where data science projects go wrong, how you should think of data science projects, what constitutes success in data science and how you can measure progress. This session will be loaded with terms, stories and descriptions of project successes and failures. If you're wondering whether you're getting value out of data science, how to get more value out of it and even whether you need it then this talk is for you!
What you will take away from this session
Learn how to make your data science projects successful
Evaluate how to track progress and report on the efficacy of data science solutions
Understand the role of engineering and data scientists
Understand your options for processes and software
Much of the thought around Lean UX focuses on design groups within product organizations (startups and enterprises). What happens when you try to use Lean design methodologies inside of an agency.
This presentation was given at the Lean UX Meetup in San Francisco on May 30, 2012.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
Topology Optimization
Topology optimization is concerned with material distribution and how the members within a structure are connected. It treats the “equivalent density” of each element as a design variable.
The solver calculates an equivalent density for each element, where 1 is equivalent to 100% material, while 0 is equivalent to no material in the element. The solver then seeks to assign elements that have a low stress value a lower equivalent density before analyzing the effect on the remaining structure. In this way extraneous elements tend towards a density of 0, with the optimum design tending towards 1. As a designer, you will need to exercise your judgment. For example, you may decide that you will omit material from all (finite) elements whose density is less than 0.3 (or 30%). Using an iso-plot of element densities helps to visualize the “remaining” structure as elements with a density below this threshold can be masked leaving behind the optimum design. Then you will need to take this geometry back to your CAD modeler, smooth it out (that is, use geometrically regular edges or surfaces, etc.) and re-evaluate the design for stresses, displacements, frequencies etc..
Lecture notes of the course Future Models I (AR1TWF030), The Why Factory, Directed by Prof. Winy Mass, TU Delft, Faculty of Architecture and Built Environment
Taking portfolio benefits management to the next level with modern analytics webinar
Wednesday 13 June 2018
presented by Ian Stuart, Altis Consulting, Principal
hosted by Merv Wyeth, Benefits Management SIG Secretary
The link to the write up page and resources of this webinar:
https://www.apm.org.uk/news/taking-portfolio-benefits-management-to-the-next-level-with-modern-analytics-webinar/
This examines the potential for the application of Design Science principles to the solution design process within solution architecture to improve the rigour and accuracy of solution designs.
Design Science is the structured and systematic process for creating designs that resolve problems. It is concerned with the structured process for the acquisition and application of knowledge in relation to the problems to the resolved and the solution knowledge to be applied.
The application of Design Science must be a means to an end – better solution quality – and not an end in itself – an incentive for the design function is to become large.
Solution architecture requires a (changing) combination of technical, leadership, interpersonal skills, experience, analysis, appropriate creativity, reflection and intuition applied in a structured manner.
Knowledge management – problem knowledge and solution knowledge – is at the core of the application of design science principles.
Knowledge management requires good management of the solution architecture function.
Lean Analytics is a set of rules to make data science more streamlined and productive. It touches on many aspects of what a data scientist should be and how a data science project should be defined to be successful. During this presentation Richard will present where data science projects go wrong, how you should think of data science projects, what constitutes success in data science and how you can measure progress. This session will be loaded with terms, stories and descriptions of project successes and failures. If you're wondering whether you're getting value out of data science, how to get more value out of it and even whether you need it then this talk is for you!
What you will take away from this session
Learn how to make your data science projects successful
Evaluate how to track progress and report on the efficacy of data science solutions
Understand the role of engineering and data scientists
Understand your options for processes and software
Much of the thought around Lean UX focuses on design groups within product organizations (startups and enterprises). What happens when you try to use Lean design methodologies inside of an agency.
This presentation was given at the Lean UX Meetup in San Francisco on May 30, 2012.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
Topology Optimization
Topology optimization is concerned with material distribution and how the members within a structure are connected. It treats the “equivalent density” of each element as a design variable.
The solver calculates an equivalent density for each element, where 1 is equivalent to 100% material, while 0 is equivalent to no material in the element. The solver then seeks to assign elements that have a low stress value a lower equivalent density before analyzing the effect on the remaining structure. In this way extraneous elements tend towards a density of 0, with the optimum design tending towards 1. As a designer, you will need to exercise your judgment. For example, you may decide that you will omit material from all (finite) elements whose density is less than 0.3 (or 30%). Using an iso-plot of element densities helps to visualize the “remaining” structure as elements with a density below this threshold can be masked leaving behind the optimum design. Then you will need to take this geometry back to your CAD modeler, smooth it out (that is, use geometrically regular edges or surfaces, etc.) and re-evaluate the design for stresses, displacements, frequencies etc..
Lecture notes of the course Future Models I (AR1TWF030), The Why Factory, Directed by Prof. Winy Mass, TU Delft, Faculty of Architecture and Built Environment
Lecture notes of the course Future Models I (AR1TWF030), The Why Factory, Directed by Prof. Winy Mass, TU Delft, Faculty of Architecture and Built Environment
Ar1 twf030 lecture2.1: Geometry and Topology in Computational DesignPirouz Nourian
Lecture notes of the course Future Models I (AR1TWF030), The Why Factory, Directed by Prof. Winy Mass, TU Delft, Faculty of Architecture and Built Environment
Preliminaries of Analytic Geometry and Linear Algebra 3D modellingPirouz Nourian
from my lecture notes for the course Geo1004 (2015), 3D modelling of the built environment, at TU Delft, faculty of Architecture and the Built Environment
Maximize Your Content with Beautiful Assets : Content & Asset for Landing Page pmgdscunsri
Figma is a cloud-based design tool widely used by designers for prototyping, UI/UX design, and real-time collaboration. With features such as precision pen tools, grid system, and reusable components, Figma makes it easy for teams to work together on design projects. Its flexibility and accessibility make Figma a top choice in the digital age.
Can AI do good? at 'offtheCanvas' India HCI preludeAlan Dix
Invited talk at 'offtheCanvas' IndiaHCI prelude, 29th June 2024.
https://www.alandix.com/academic/talks/offtheCanvas-IndiaHCI2024/
The world is being changed fundamentally by AI and we are constantly faced with newspaper headlines about its harmful effects. However, there is also the potential to both ameliorate theses harms and use the new abilities of AI to transform society for the good. Can you make the difference?
Visual Style and Aesthetics: Basics of Visual Design
Visual Design for Enterprise Applications
Range of Visual Styles.
Mobile Interfaces:
Challenges and Opportunities of Mobile Design
Approach to Mobile Design
Patterns
Transforming Brand Perception and Boosting Profitabilityaaryangarg12
In today's digital era, the dynamics of brand perception, consumer behavior, and profitability have been profoundly reshaped by the synergy of branding, social media, and website design. This research paper investigates the transformative power of these elements in influencing how individuals perceive brands and products and how this transformation can be harnessed to drive sales and profitability for businesses.
Through an exploration of brand psychology and consumer behavior, this study sheds light on the intricate ways in which effective branding strategies, strategic social media engagement, and user-centric website design contribute to altering consumers' perceptions. We delve into the principles that underlie successful brand transformations, examining how visual identity, messaging, and storytelling can captivate and resonate with target audiences.
Methodologically, this research employs a comprehensive approach, combining qualitative and quantitative analyses. Real-world case studies illustrate the impact of branding, social media campaigns, and website redesigns on consumer perception, sales figures, and profitability. We assess the various metrics, including brand awareness, customer engagement, conversion rates, and revenue growth, to measure the effectiveness of these strategies.
The results underscore the pivotal role of cohesive branding, social media influence, and website usability in shaping positive brand perceptions, influencing consumer decisions, and ultimately bolstering sales and profitability. This paper provides actionable insights and strategic recommendations for businesses seeking to leverage branding, social media, and website design as potent tools to enhance their market position and financial success.
1. 11
On Computational Design
An overview of essential topics and approaches
Dr.ir. Pirouz Nourian
Assistant Professor of Design Informatics
Department of Architectural Engineering & Technology
Faculty of Architecture and Built Environment
2. 22
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
WYSIWYG versus WYSIWYM
𝑥2
+ 𝑦2
= 𝑅2
The Product vs The Process
3. 33
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Parametric Modeling & Design
• Thinking of parameters instead of numbers!
• Same rationales, many alternatives!
▪ We could model an actual circle as a particular instance of a generic circle, which is
the locus of points equidistant from a given point as C (center), at a given distance R
(Radius), on a plane p.
▪ Parametric modeling is essential for formulating design problems
▪ The same role algebra has had in the progress of mathematics, parametric modeling
will have in systematic (research-oriented) design.
𝑥 = 𝑟𝑐𝑜𝑠(𝑡)
𝑦 = 𝑟𝑠𝑖𝑛 𝑡
𝑡 ∈ [0,2𝜋]
𝑡 =
2𝜋𝑖
𝑛
|𝑖 ∈[1,n]⊂ ℕ
Plane
Radius
Circle
4. 44
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Hierarchical Decision Making
Synthesis SynthesisSynthesis
Evaluation Evaluation Evaluation
Structural Logic
Shape
Structure Details
Materials
Construction
Analysis Analysis Analysis
Phase 0:
Design Intent
Phase 1:
Design Development
Phase 2:
Detail Design
Climatic Logic
Functional Logic Configuration
5. 55
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
How to reach at good solutions out of many alternatives
(optimization/control techniques)
• Identification of spatial/physical design principles
• definition of design goals in terms of performance
criteria; and defining a phase-model for the parametric
design process: from schematic design to detailing;
• formulation of ‘design problems’ (parameterization);
• parametric generation of design alternatives (in
collaboration with Architect, Structural Designer,
Designer Building Services, Façade Designer and Project
Manager);
• performance measurements (again in collaboration with
the other team members);
• design optimization (maximization of desired performance
measures)
Exploration/Optimization Framework
Goals Principles Formulation Evaluation
6. 66
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Common Misconceptions!
• we can automate the design process!
• parametric design is another architectural style!
• parametric design= grasshopper!
• computational design is a magic art!
• computational design is for geek guys!
7. 77
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Using sophisticated software applications does NOT
necessarily mean doing computational design!
• If we start with wrong assumptions at the beginning, a simulation tool cannot
tell us what to do to improve our design!
• Even if we optimize minor things at a late stage of design, the whole
configuration might be extremely ineffective and inefficient due to initial
decisions!
• Most important decisions pertained to configuration and shape are made at
early stages of design process!
Design
(CAD)
Simulate
(FEA)
Label!
(LEED)
Certified—45
points
Silver—60 points
Gold—75 points
Platinum—90 points
8. 88
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Why Computational Design?
What is it that we could not do without computation?
How do we design for better life (more sustainable if you like)?
But what is good??? And how do we compare actual design alternatives?!
How do we know if our design is going to work as intended?
How can we underpin our design as to its functional rationale?
What is it that we could do better with computation?
9. 99
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
How do we design methodically?
10. 1010
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
How do we design methodically?
1. Create (synthesize) objects systematically
2. Measure (analyze/simulate) qualities quantitatively
3. Compare (evaluate) designs objectively
11. 1111
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
How do we design methodically?
1. Create (synthesize) objects systematically through:
• parametric formulation of phenotypes
• systematic generation of genotypes
2. Measure (analyze/simulate) qualities quantitatively
• Analysis using mathematical models, non-contextual
• Simulation using computational models, contextual
3. Compare (evaluate) designs objectively using:
• Absolute Extremums
• Standards/Milestones/Benchmarks
• Evaluation Frameworks
12. 1212
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Design Process
• Design is about making things
• Science is about knowing things
• Design is about working on vague problems that have no definitive solution!
• A design is a concrete proposition for an abstract demand.
• Philosophically, there can never be a proof that a design is the best it could ever be!
(Rittel, 1973)
• Formulation is as important as problem-solving. (Simon, 1999)
• Design is a process of co-evolution of problems and solutions (Cross & Dorst 2007),
through analysis, synthesis and evaluation (Lawson, 2005).
13. 1313
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Design Process
The process of “thinking” for “making” something based on needs,
intentions, requirements and constraints.
Cross & Dorst 2007
Lawson 2005
14. 1414
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
What is it? Who is she?
15. 1515
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Computation
Computers and computer are causal systems; meaning, programs and computers
DO NOT THINK in the sense that human beings do; they do not have intentions,
motives, anticipation or creativity: they just act as programmed!
Information processing
by means of algorithms
An algorithm is
a technical recipe for doing something
Image courtesy of http://iheartapple.com
16. 1616
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
To avoid misconceptions:
Analysis and Simulation are meant to provide an indication of Performance (Functionality) of an
Environment by means of Mathematical Models or Computational Models (respectively).
Analytic or Simulated Performance measurements are neutral per se.
Evaluation is a step above Analysis and Simulation that is to conclude with a judgement on the
relative quality of a building/’design’ compared to other buildings/’designs’.
Optimization is the systematic process of seeking the highest attainable level of quality.
Optimization processes are generally either set up as feed-forward or feed-back control
mechanisms.
17. 1717
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Performance Analysis
Measuring potentials, 80% mathematical-20% computational
• Continuous Models:
Analytic measurements using
mathematical models of objects, e.g.
curvature analysis
• Discrete Models:
Analysis of walkability by finding
distances on a network using optimal
path algorithms
18. 1818
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Performance Simulation
Measuring dynamics, 20% mathematical-80% computational
How does a ‘system’ behave (affects or gets affected by) in a particular ‘environment’?
For example, how much a certain building will be lit throughout winter in Amsterdam?
Agent Simulation, image courtesy of Space Syntax LtdSolar Gain Estimation
19. 1919
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Performance Evaluation
How good a system is behaving/performing?
• Quality criteria
• A quantitative interpretation of performance simulations/estimations
• How to tell if design A is performing better than design B?
• Defining an “objective function”
Solar Gain Estimation and Evaluation: comparing a set of different design alternatives for a courtyard housing block
20. 2020
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
A) Continuous Changes:
• Feed-Forward using mathematical analysis or computational simulation
• Feed-Back using meta-heuristic methods such as evolutionary algorithms, simulated
annealing, swarm intelligence, etc.
Parametric
Circle
Radius𝑟 = ൗ𝐴
𝜋
A 100 𝑚2
big circle
Parametric
Circle
Radius circle
Manipulate R
to minimize Δ
Compute
Area
How do we make a circle that is as big as 100 𝑚2
?
21. 2121
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
B) With Topological Changes
Catalogue/Enumerate and Rank listing all(most important) possibilities
What layout topologies are possible for our configuration? And which of them is the best…
A syntactic architectural design methodology, Nourian et al, 2013
22. 2222
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
▪ Problem Formulation:
• Defining Design Goals
• Determining the Hierarchy of Goals and their Corresponding Decisions
• Formulating Design Principles (multiple disciplines)
• Ideation for Integrating Design Principles in a Configuration
• Identifying Trade-Offs and Formulating Optimization Problems
• Algorithmic Sketching of the Idea
▪ Design Development:
• Designing a Computational Workflow
Mathematical Interpretation
Identifying Systems and Sub-systems
Drawing Flowcharts
Writing Pseudocode
• Programming/Workflow Modelling
▪ Problem Solving:
• Feed-Forward Optimization
• Feed-Back Optimization
23. 2323
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
▪ Problem Formulation:
• Defining Design Goals
• Determining the Hierarchy of Goals and their Corresponding Decisions
• Formulating Design Principles (multiple disciplines)
• Ideation for Integrating Design Principles in a Configuration
• Identifying Trade-Offs and Formulating Optimization Problems
• Algorithmic Sketching of the Idea
▪ Design Development:
• Designing a Computational Workflow
Mathematical Interpretation
Identifying Systems and Sub-systems
Drawing Flowcharts
Writing Pseudocode
• Programming/Workflow Modelling
▪ Problem Solving:
• Feed-Forward Optimization
• Feed-Back Optimization
Watch at home!
24. 2424
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
▪ Problem Formulation:
• Defining Design Goals
• Determining the Hierarchy of Goals and their Corresponding Decisions
• Formulating Design Principles (multiple disciplines)
• Ideation for Integrating Design Principles in a Configuration
• Identifying Trade-Offs and Formulating Optimization Problems
• Algorithmic Sketching of the Idea
▪ Design Development:
• Designing a Computational Workflow
Mathematical Interpretation
Identifying Systems and Sub-systems
Drawing Flowcharts
Writing Pseudocode
• Programming/Workflow Modelling
▪ Problem Solving:
• Feed-Forward Optimization
• Feed-Back Optimization
IN OUT
A CAUSAL SYSTEM
Nourian, Rezvani, Sariylidiz, 2013, Space Syntax for Generative Design
25. 2525
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
▪ Problem Formulation:
• Defining Design Goals
• Determining the Hierarchy of Goals and their Corresponding Decisions
• Formulating Design Principles (multiple disciplines)
• Ideation for Integrating Design Principles in a Configuration
• Identifying Trade-Offs and Formulating Optimization Problems
• Algorithmic Sketching of the Idea
▪ Design Development:
• Designing a Computational Workflow
Mathematical Interpretation
Identifying Systems and Sub-systems
Drawing Flowcharts
Writing Pseudocode
• Programming/Workflow Modelling
▪ Problem Solving:
• Feed-Forward Optimization
• Feed-Back Optimization Configraphics: Graph Theoretical Methods of Design and Analysis of Spatial Configurations,
Nourian, P, 2016
26. 2626
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
▪ Problem Formulation:
• Defining Design Goals: Pen & Paper
• Determining the Hierarchy of Goals & Decisions: Rationalization
• Formulating Design Principles: Diverge, Agree to Disagree, Abstract & Generalize
• Ideation for a Configuration: Converge and Synthesize One Solution
• Identifying Trade-Offs and Formulating Optimization Problems: Pen & Paper
• Algorithmic Sketching of the Idea: parametrize the idea or define it based on rules
▪ Design Development:
• Designing a Computational Workflow
Mathematical Interpretation: pen & paper
Identifying Systems and Sub-systems
Drawing Flowcharts: www.draw.io
Writing Pseudocode: pen & paper
• Programming/Workflow Modelling
▪ Problem Solving:
• Feed-Forward Optimization: genotype creation (e.g. by network configuration)
• Feed-Back Optimization: phenotype evolution (e.g. by genetic algorithms)
27. 2727
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Processing & OSGeo
Generative Components on Micro Station
Viz on SketchUp
Node Editor on Blender
Marionnette on Vector Works
28. 2828
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Collaborative Workflow
Technical Integration of Designs and Building Information Model
https://flux.io/
29. 2929
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Plugins that we recommend:
Structural Design Computations:
• Kangaroo
• Millipede
• Karamba
30. 3030
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Plugins that we recommend:
Climatic Design Computations:
• Ladybug
• DIVA
• ArchSim
31. 3131
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Plugins are out there:
BIM Plugins
• Geometry Gym
• GH>>Revit
• Visual ARQ
• Chameleon
32. 3232
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Plugins that we recommend:
Topological Mesh Modelling
• Mesh Edit (UTO)
• Weaver Bird
• Leopard
• Mesh(+)
33. 3333
• What it is
• What it is not
• Why
• How
o Theory
o Practice
• Design Process
• Computation
• Terminology
o Analysis
o Simulation
o Evaluation
o Optimization
• Methods
• Techniques
• Platforms
• Tools
Plugins that we recommend:
Architectural Design Computations
• Spider Web
• Syntactic (Space Syntax)