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
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
CreativeAI & Deep Learning Research
2020
Looking into development of generative networks capable of learning relevant semantic features of La Sagrada Familia at different scales.
#generativeadversarialnetworks #nonstandardstudio
#neuralnetworks #deeplearning
2019
#generativeadversarialnetworks #nonstandardstudio
#neuralnetworks #deeplearning #agentbasedsystem
2019
Looking into development of generative networks capable of learning relevant semantic features of La Sagrada Familia at different scales.
#generativeadversarialnetworks #nonstandardstudio
#neuralnetworks #deeplearning
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
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
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…
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
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.
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
[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.
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
[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
[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.
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
[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
[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.
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
[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
[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.
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
[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.
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
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
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
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
Small hack into BZR – Belousov Zhabotinsky Reaction, a more continuous differentiated model
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
[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.
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
[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.
Space Attractor – Internalized Feedback
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Music: | Noemi Bolojan – Shine upon me | soundcloud.com/noemibolojan
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.
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.
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 to correlate multiple systems, and encourage contributive coexistence of different articulation layers.
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
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help us to spread the word by sharing this with your friends on facebook, twitter, Tumblr etc.
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
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.
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.
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.
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
watch it fullscreen
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.