nonstandardstudio

Jan 2020

Meet DeepHimmelblau

Meet Deep Himmelb(l)au. Our algorithm learns CHBL’s semantic characteristics to generate new interpretations and new worlds.

Great improvement of the DeepHimmelb(l)au network that I’ve been building recently at Wolf D. Prix / Coop Himmelb(l)au. DeepHimmelblau is now able to learn an enormous amount of semantic characteristics to create more detailed interpretations #computervision #deephimmelblau #deeplearning

(Video: Our algorithm leanrs CHBL’s semantic charateristics to generate new interpretations and create a new language of architecture)

What is DeepHimmelblau?

DeepHimmelb(l)au is the result of the cumulative research effort undertaken by Coop Himmelb(l)au which operates at the intersection between architecture research, practice and Ai/deep learning.

DeepHimmeb(l)au is an experimental research project led by Design Principal Wolf D. Prix, Design Partner Karolin Schmidbaur and Chbl’s Computational Design Specialist Daniel Bolojan, which explores the potential of teaching machines to perceive, interpret and generate new designs of buildings, augment design workflows and augment the architect’s / designer’s creativity.

DeepHimmelb(l)au is currently the most advanced research dealing with the design potential of AI/deep learning undertaken by any architectural office.

What is DeepHimmelblau’s main aim?

Marshall McLuhan had a very interesting comment about the relationship between the creator / designer and his operating medium / tools – ”First we shape our tools, thereafter they shape us”. Similarly, the research inquires about the possible impact of Ai on the role of architects / designers and the relationship between new technologies / tools and designers. What role can Ai play in the design process? Should the role of Ai be to replace architects/designers? Or should it have a design assistant role interacting with designers/architects to augments design workflows and creativity?
For decades it has been the design methodology employed by Coop Himmelb(l)au to work within an open process, driven by an architectural idea and utilizing a multitude of available tools, that finds precisely in the productive translation from one medium to another inspiration for further steps in the development of the project. The individual outputs are often used in a similar fashion like a found object, an ‘objet trouvé’, to inspire new ways of perception and articulation through their interpretation and translation into architectural vocabulary.
One of the main focuses of Coop Himmelb(l)au’s current research on artificial intelligence is the concept of Augmented Creativity, where Ai is used as a new medium within that overall design methodology. We are examining the disruptive paradigm shift driven by the introduction of generative Ai methods in architectural design. Could Ai support the designer’s creativity?
We are currently developing a DeepHimmelb(l)au network, not as a means to automate a designer’s creativity, but rather to augment it per the methodology described above. DeepHimmelb(l)au is designed to interact with designers and inspire creativity. It is designed to facilitate a medium of constant interaction / feedback loops between designer interpretations and its own interpretations, between designer perceptions and its own perceptions. In that sense we are aiming – through augmentation – to strengthen our capabilities as creators – a collaboration between machines and humans.
While developments in Ai mean computers can be trained on certain creativity criteria, the degree to which Ai can develop its own sense of creativity is still something to investigate. Can Ai be taught how to create without guidance? Can Ai be taught how to interpret things? Can we, with help of AI, build the intelligence of the hand into an all digital design process? Can Ai be taught how to reinterpret representations from one domain to another, similar to how architects are inspired by concepts outside their architectural domain? Teaching computers to be creative is inherently different from how people create, but we do not yet know much about our own creative methodology.
Our perceptions and our conscious visual representations of reality are not a direct mapping of the real world. Humans interpret reality through reconstructions and interpretations based on past experiences. Our past experiences act as a frame / filter on our way of interpreting, understanding and perceiving the real world. Our training as architects operates as a filter / frame in the way we perceive the world, the way we interpret it and the way we draw inspiration from it.
One very common practice in design and architecture is that a designer learns, consciously or unconsciously, semantic representations of one domain, reinterprets that representation through a particular filter e.g. architectural style, architectural culture etc., and translates it to a different domain.
While humans unconsciously are capable of recognizing and disentangling various semantic features of what they perceive, neural networks are capable of having similar behavior after learning from a large enough set of samples. Some Networks learn automatically to separate/disentangle various semantic features of a dataset and afterwards enable specific features to be separated and managed on a particular level. In addition, machines exposed to large sample sets can discover perceptual deficiencies in human recognition capabilities. Can this innate capacity augment the creativity and interpretation of the designer?

AI learning CHBL’s semantic charateristics

AI interpretation of a Coop Himmelb(l)au massing model

Team CHBL

Design Principal: Wolf D. Prix

Design Partner: Karolin Schmidbaur

Computational Design Specialist: Daniel Bolojan

Dec 2018

Structurally Encoded Agents

Structurally Encoded Agents – Using a neural network to train a multi-agent system to be structurally aware and encode structural heuristics as behaviors within an agent.

Untitled-1_0002_Layer 1 Untitled-1_0001_Layer 2 Untitled-1_0000_Layer 3

Oct 2018

Design Space Explorer – Multi-agent Systems

This approach represents a shift from the idea of design as an output to the idea of design as multiple outputs, designer engaging into a process of selection.

The role of Creativity in this design process is to define the constraints that generate a range of possible solutions for a design problem, and define and develop strategies and effective methods of filtering and evaluating possible solutions.

It’s no more a question of defining the ideal design, but rather of reviewing the entire design space of possible sample outcomes, selecting and enhancing the best solution.

(P.S. this is a w.i.p strategy)
#creativeAi #neuralnetworks #multiagentsystems #designspaceexplorer #nonstandardstudio

Feb 2018

Agent based systems coupled with machine learning algorithms

Current on-going research focusing on unsupervised learning algorithms in order to classify and cluster behavioral inputs and integrate them within the design process, as a form-finding phase through a non-linear morph of behavior parameters.

AGENT BASED SYSTEMS COUPLED WITH MACHINE LEARNING ALGORITHMS

The selected 8 models are used as inputs for the SOM. The resulting map creates an interpolation between inputs and creates a location rule, placing similar models closer on the map while placing further apart dissimilar models.

AGENT BASED SYSTEM TRACE FOLLOW BEHAVIOR PARAMETERS

VIEW ANGLE

COHESION MAGNITUDE

COESHION VIEW RANGE

SEPARATION MAGNITUDE

SEPARATION VIEW RANGE

FIELD MIN INTENSITY

FIELD MAX INTENSITY

STEP MAGNITUDE

OVERALL SYSTEM LENGTH

COLOR START

COLOR END

BEHAVIOR PARAMETERS INFLUENCE DISTRIBUTION

K-mean clustering is used here to analyze parameter distributions on the self-organizing map (SOM), and help cluster and classify insightful behavioral patterns, and quantify the influence each has on the outcome.

#java #python #tensorflow #som #unsupervisedlearning #multiagentalgorithms #behavior

Gaudi + Neural Networks

Venice Biennale

Deployment of Deep Learning strategies to liberate the intricacy, novelty and structural properties found in the tectonic detail of Sagrada Familia.

NONSTANDARDSTUDIO

CreativeAI & Deep Learning Research

Gaudi + Neural Networks

2020

Looking into development of generative networks capable of learning relevant semantic features of La Sagrada Familia at different scales.

#generativeadversarialnetworks #nonstandardstudio
#neuralnetworks #deeplearning

Agent Based DCGAN

2019

#generativeadversarialnetworks #nonstandardstudio
#neuralnetworks #deeplearning #agentbasedsystem

DeepLearning Sagrada Familia

2019

Looking into development of generative networks capable of learning relevant semantic features of La Sagrada Familia at different scales.

#generativeadversarialnetworks #nonstandardstudio
#neuralnetworks #deeplearning

Design Space Explorer

This approach represents a shift from the idea of design as output to the idea of design as multiple outputs, designer engaging in selection process.

The role of Creativity in this design process is to define the constraints that generate a range of possible solutions to the design problem, and to define and develop strategies and effective methods for filtering and evaluating possible solutions.

#deeplearning #agentbasedsystems #nonstandardstudio

DeepHimmelblau Research

Meet Deep Himmelb(l)au. Our algorithm learns CHBL’s semantic characteristics to generate new interpretations and new worlds.

#computervision #deephimmelblau #deeplearning

Mar 2020

3D Domain Translations

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3D Domain Translation using Cycle-Consistent Adversarial Networks

Some of the algorithms I started developing while at I.sd Institute of Structure and Design, Innsbruck.

The 3D Domain Translation model starts to show some promising results. Although still more work is needed to be done to allow for a better disentanglement of features.

This approach builds on the work of Jun-Yan Zhu, Taesung Park, Philip Isola, Alexei A. Efros from Berkeley Ai Research (Bair) Laboratory, UC Berkeley

More detailed description soon…

Aug 2017

Machine Learning for generative design

In this case, the autoencoder network is used to perform the task of dimensionality reduction, in combination with the parameters of a generative system (woolly threads), in order to visualize the high dimensional parameter space of these sort of system.

Self-organizing maps (SOM) are used here, (SOM represents a type of unsupervised artificial neural network), to reduce the high dimensionality of the data whilst also retain the high-dimensional non-linear associations. As SOMs can create associations between inputs, the map can suggest an overview of possibilities within the given parameter space, without the need of manually tweaking the system’s parameters by the designer.
The selected 7 models are used as inputs for the SOM. The resulting map creates an interpolation between inputs and creates a location rule, placing similar designs closer on the map while placing further apart dissimilar designs.

Parameters
– cutoff parameter
– stiffness parameter
– numToMove parameter
– num of control points parameter
– color parameter
– root location

#autoencoder #owl #machineLearning

As in the previous post, because the dataset was relatively small, I used only CPU ( 2 x Intel Core i7-4930k @ 3.40GHz) computing for training phase. If you are dealing with a big data set, a GPU might be a better way to go to train your dataset.

SNA – Machine Learning – Dimensionality reduction

Visualization of a social network analysis (SNA) defined by a high-dimensional parameter space (21 dimensions). Dimensionality reduction (DR) is used here to allow the reduction of the number of variables that defines a system, into a lower-dimensional space (2D). In this case, I’m using self-organizing maps (SOM), which represent a type of unsupervised artificial neural network, to reduce the high dimensionality of the data whilst also retain the high-dimensional non-linear associations.

Another feature extraction used (side by side image), is a linear feature extraction method -> K-means cluster analysis, (although linear methods don’t retain the associations distributed non-linearly in the high dimensional space). #owl #machinelearning

As the dataset was relatively small, I used only CPU ( 2 x Intel Core i7-4930k @ 3.40GHz) computing for the training phase. If you are dealing with a big data set, a GPU might be a better way to go to train your dataset. CPU – 2 x Intel Core i7-4930k @ 3.40GHz GPU – GPU

03_UnSupervised Learning - autoencoder 577 tensors 002.jpg

03_UnSupervised Learning - autoencoder 577 tensors 004

03_UnSupervised Learning - autoencoder 577 tensors 012.jpg

03_UnSupervised Learning - autoencoder 577 tensors 001.jpg

03_UnSupervised Learning - autoencoder 577 tensors 010.jpg

03_UnSupervised Learning - autoencoder 577 tensors 013.jpg

GPGPU – Agent bodies facade

Investigations into agent bodies algorithms, and behavioral algorithms, where gradients and levels of differentiation are not predefined but rather a result of emergence.
#GPGPU #Stigmergy #Java #agentbodies #selforganization #JCuda #sematectonicFields nonstandardstudio

more description soon…

There were 2xNVidia GTX780Ti used in this study, having in total a combined 5760 CUDA cores (2880 each), running on a machine 2 x Intel Core i7-4930k @ 3.40GHz and Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz.

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

[3] Bonabeau E., M. Dorigo, G. Théraulaz. Swarm Intelligence: From Natural to Artificial System s. Santa Fe Institute in the Sciences of the Complexity, Oxford University Press, New York, Oxford, 1999.
[4] Chialvo, Dante R., Millonas, Mark M. “How Swarms Build Cognitive Maps” . In Luc Steels (Ed.), The Biology and Technology of Intelligent Autonomous Agents , (144) pp. 439-450, NATO ASI Series, 1995.
[5] Vitorino Ramos, Filipe Almeida, Artificial Ant Colonies in Digital Image Habitats – A Mass Behaviour Effect Study on Pattern Recognition, Proceedings of ANTS’2000 – 2nd International Workshop on Ant Algorithms (From Ant Colonies to Artificial Ants), Marco Dorigo, Martin Middendorf & Thomas Stüzle (Eds.), pp. 113 – 116, Brussels, Belgium, 7 – 9 Sep. 2000.

US equivalent
GPU: 2xNVidia GTX780Ti
CPU: 2 x Intel Core i7-4930k @ 3.40GH
RAM: Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

Structural Stigmergy Model

Structural Stigmergy Model nonstandardstudio #stigmergy #structuralStigmergy #shellStructures #catenary

Articulation of structural morphologies – through principles of stigmergic collaboration and structural coupling.

By self-regulating critical variables of force distribution within a structural system, the ecology of agents is developing an automatic mechanism of self-regulation – homeostasis/autopoiesis system – where the system becomes self-producing, allowing for a correlated and differentiated multi-system. Where like in natural systems, compositions are so highly integrated that they cannot be easily decomposed into independent subsystems. It becomes a system with self-reference and self-regulation which further evolves by using structural coupling. Therefore recognizing that the outside influences cannot shape the system’s internal structure, but act only as a trigger that causes the structure to alter its current attractors.

The ecology of agents allows us to correlate multiple systems, and encourage the contributive coexistence of different articulation layers.

There were 2xNVidia GTX780Ti used in this study, having in total a combined 5760CUDA cores (2880 each), running on a machine 2 x Intel Core i7-4930k @ 3.40GHz and Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz.

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

US equivalent
GPU: 2xNVidia GTX780Ti
CPU: 2 x Intel Core i7-4930k @ 3.40GH
RAM: Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

GPGPU – Structurally encoded agent bodies

Couple iterations of Pavilion studies that we did last year. Full project pictures will be presented soon.

#agentbodies #swarm #swarmintelligence #informedbodies #chargedfields #java #jCuda nonstandardstudio

more description soon…

There were 2xNVidia GTX780Ti used in this study, having in total a combined 5760CUDA cores (2880 each), running on a machine 2 x Intel Core i7-4930k @ 3.40GHz and Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz.

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

US equivalent
GPU: 2xNVidia GTX780Ti
CPU: 2 x Intel Core i7-4930k @ 3.40GH
RAM: Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

GPGPU – Sematectonic Fields – Smart Agent Bodies

The study explores the idea of agent bodies where the body of the agent is defined by a mesh geometry capable of reacting to different stimuli. The agent body has its own behavioral rules, that determine the way it will interact and connect with other neighbors’ bodies.

There are two levels of interactions, therefore, the interaction between agents, and interactions between agent bodies. The bodies are intelligent enough to be able to adapt to local conditions, interactions. Their perception and memory are not only self-gathered but also inherited from the agent swam. Series of smart agent bodies start to snap, interact and connect to each other giving rise to emergent gradient patterns, and intense levels of differentiation.

Because of the high amount of agents, and interactions involved, and to satisfy the required computational power, necessary for these sorts of processes, swarm algorithms adapted for GPGPU were deployed.

There were 2xNVidia GTX780Ti used in this study, having in total a combined 5760CUDA cores (2880 each), running on a machine 2 x Intel Core i7-4930k @ 3.40GHz and Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz.

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

US equivalent
GPU: 2xNVidia GTX780Ti
CPU: 2 x Intel Core i7-4930k @ 3.40GH
RAM: Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

Feb 2016

Ubiquitous nearness ‪

Running on a machine 2 x Intel Core i7-4930k @ 3.40GHz and Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz.

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

US equivalent
GPU: 2xNVidia GTX780Ti
CPU: 2 x Intel Core i7-4930k @ 3.40GH
RAM: Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

Saturation – Information Rich Environments

Running on a machine 2 x Intel Core i7-4930k @ 3.40GHz and Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz.

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

Internalized feedback

Voxel space resolution – 600x600x600

running on a machine 2 x Intel Core i7-4930k @ 3.40GHz
with memory Corsair 64GB DDR4 DRAM

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

US equivalent
CPU: 2 x Intel Core i7-4930k @ 3.40GH
RAM: Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

Space Attractor – Internalized Feedback – Symmetrical propensity

Voxel space resolution – 600x600x600

running on a machine 2 x Intel Core i7-4930k @ 3.40GHz
with memory Corsair 64GB DDR4 DRAM

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

US equivalent
CPU: 2 x Intel Core i7-4930k @ 3.40GH
RAM: Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

Voxel space resolution – 600x600x600

running on a machine 2 x Intel Core i7-4930k @ 3.40GHz
with memory Corsair 64GB DDR4 DRAM

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

Voxel space resolution – 600x600x600

running on a machine 2 x Intel Core i7-4930k @ 3.40GHz
with memory Corsair 64GB DDR4 DRAM

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

PollenSimulation – 20k pollen particles GH

Nonstandardstudio’s Gh addon that enables a more generative/rule-based approach. Simulating flows of pollen particles.

PollenSimulation
– 20k pollen particles GH
– wind Field res 1000×800

https://youtu.be/dqsyaW-Rsy0

BZR – Belousov Zhabotinsky Reaction

Small hack into BZR – Belousov Zhabotinsky Reaction, a more continuous differentiated model

Agent Morphologies

Supercomputing (GPGPU) – Stigmergy systems

There were 2xNVidia GTX780Ti used in this study, having in total a combined 5760CUDA cores (2880 each), running on a machine 2 x Intel Core i7-4960X @ 3.60GHz and Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz.

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

Swarm bodies : java+jcuda

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

Space Attractor – Internalized feedback

Space Attractor – Internalized Feedback

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Music: | Noemi Bolojan – Shine upon me | soundcloud.com/noemibolojan

Agent Morphologies

Stigmergic Morphologies

an ongoing exploration of stigmergy systems where the agents interact with their own pheromone traces and control local dynamic information to influence and adjust larger urban life-processes by embedding intelligence into the formation, organization, and performance of urban spaces.

Feb 2015

Tectonic Articulation

Bearing in mind the three dimensions of architecture task -organization, articulation, signification- and in an effort to create a structural autopoietic system that will offer new opportunities for architectural articulation, the study explores articulation of structural morphologies -through principles of stigmergic collaboration and structural coupling- that will allow the articulation between a variety of spaces by transitions through structural tectonic static morphing.

The ecology of agents allows to correlate multiple systems, and encourage contributive coexistence of different articulation layers.

Sematectonic Urban Fields

ongoing research on Sematectonic urban fields
Multi-agent systems (M.A.S) (3 billion particles)

Feb 2014

VVVV displacement studies

wip …noisy agents…130 million cells

surface active tesselation 004

Jun 2013

Studio Zaha Hadid Vienna workshop

I’m pleased to announce that Studio Zaha Hadid Vienna is organizing a two-week, intensive investigation workshop into Parametric Urbanism. Studio Zaha Hadid Vienna workshop will be led by:

– Ass. Dipl.-Ing.MArch. AA Dist. [Robert NEUMAYR-BEELITZ] – lecturer/critic

– AProf. Mag.arch. Mag.theol. [Johannes TRAUPMANN] – critic

– Univ.-Ass. Dipl.-Ing. [Jens Erik MEHLAN] – critic

– Univ.Stud.Ass. [Daniel BOLOJAN] – grasshopper tutor

– Univ.Stud.Ass. [Bogdan ZAHA] – Maya tutor, all from Studio Zaha Hadid Vienna.

As part of ongoing academic research, the workshop will introduce participants into a contemporary discussion of formal exploration in urban design methodologies enacted through the medium of computation.

more information on blog

more information on facebook

APPLY @ : info@edukube.org

help us to spread the word by sharing this with your friends on facebook, twitter, Tumblr etc.

ubiquitous urbanism studio zaha hadid

Our project explores principles of physical and visual connectivity as a method of evaluating and generating new spatial solutions for contemporary society. This idea derives from startup research of individual urban systems, where on the example of working environments, we have addressed problems, needs, and desires of corporative field.

Having analyzed existing precedents, we have met certain communicational constraints intrinsic to old spatial models, that still widely repeated even for new social processes. In order to overcome these limitations, we extract local rules that affect and regulate the spatial and communicative formation of the working environment – rules of physical and visual connectivity. Implementation of these rules allows us to achieve a certain set of variations and combinations, going from complete absence of visual and physical integration between the spaces, to visual integration only, and to the maximum physical and visual integration. Being tightly related to the proximity of spaces, these internal rules have certain potential in terms of building relationships not only on local but also on a global scale.

Our idea is to unfold these rules on an urban level and encourage the correlation of multiple systems by means of principles that would be shared by all systems.

That’s why, when we move from research of individual systems to the scale of a master plan, as a very first step, we embed principles of physical and visual integration in the urban field to guarantee desired levels of connectivity and only after that gradually introduce individual systems. Thus the influence of the mentioned principles propagates through any system build upon it and acts as a correlative mechanism.

After a layer of shared properties is established, we start to build an urban field with the dominance of one system. This field is gradually altered and updated by the information of the system newly introduced in the field. Each system acts in limits that had been already ascribed to it by other teams’ research, and cause particular changes when it influences the field.

This strategy allows us to correlate multiple systems, in the way, which excludes simple collage or imposition, and encourage the contributive coexistence of urban layers.

Daniel Bolojan

Daniel Zakharyan

Mar 2013

Parametric semiology – strange attractors – stress resposiveness

Mar 2013

parametric semiology – field studies

Mar 2013

parametric semiology – surface active tesselation 003

Project Design: Daniel Bolojan, Bogdan Zaha, Robert Loffler
Studio Directors: Prof. Zaha Hadid, Patrik Schumacher, Hannes Traupman, Jens Mehlan, Robert R. Neumayr, Mario Gasser, Christian Kronaus, Mascha Veech

The project’s main focus is to solve the articulations between a variety of spaces by transitions trough structural tectonic static morphing. We are trying by this, to exploit the advantages of a tensioned structure in producing optimized force flows in its geometry and permitting to achieve a great sense of dynamism.

The transformations are based on a series of transitions between tensile structures trough principles of decomposition and re-composition by the tensegrity concept, superimposition, and juxtapositions. The primary operations undertaken here in this project are based on surface-active deformations. This enabled us to reach a state of high dynamism in shape and composition. The articulation between multiple levels of tectonic superimposition is facilitated by this process. At the same time a better communication within the architectural overall language is obtained. This system is also offering a high degree of coherent tessellation, which in accordance with the architectural logic enhances the overall quality of the space, permitting a coherent reading of the space also by the use of perforation or pattern information inscribed within the surface.

The different tectonic properties are necessary to obtain a higher degree of atmospheric values and manifold semiotic information.

Mar 2013

13th International Architecture Exhibition Venice

Mar 2013

Parametric semiology – surface active tesselation 002

The different tectonic properties are necessary to obtain a higher degree of atmospheric values and manifold semiotic information.

Mar 2013

Exoskeleton

Apr 2012

surface active tessellation

here is a first sketch that I’ve made for our first semester project of this year, where we’re investigated shell structures…this is just the primary state as I said, hopefully I will post also the recent state of this studies later this semester

Apr 2012

die angewandte processing workshop studio zaha hadid

Recently I’ve tutored a Processing workshop for Studio Zaha Hadid master class from Die Angewandte Kunst Vienna . For those how participated at the workshop, you can find source code, with all the comments and also the presentation here…make sure you register first as I explained to you via studio email. For now the source code will be available only for students from studio Zaha Hadid master class Die Angewandte Kunst Vienna . Later on I will make them public for everyone interested.

Feb 2012

swarm fields studies

beside primary rules of swarms, like cohesion, alignment, separation I started to add into this mess of particles, also some attractive force such as gravity or repulsive force/ orbital which will spin either clockwise or counterclockwise, . While on the first example I was playing exclusively with swarm primary rules, for the second example I started to add new rules (those that I mentioned above)…

watch it fullscreen

Oct 2011

Speleothem nss::.

Oct 2011

Crystallization nss::.

Oct 2011

noisiness nss::.

Oct 2011

Belousov–Zhabotinsky reaction 002 nss::.

Oct 2011

Belousov–Zhabotinsky reaction 001 nss::.

Oct 2011

noisy agents 003 nss::.

Oct 2011

noisy agents 002 nss::.

Oct 2011

noisy agents 001 nss::.

I’ve just manage somehow to recover my 1tb damaged hard-drive data…FINALLY! …I will start to post a few studies I’ve been working on, early this summer, most of them written in processing using mainly toxiclibs library (many thanks to Karsten “toxi” Schmidt ). So ya…enjoy it.