nonstandardstudio
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.
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.
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 Translations
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…
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
[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.
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
[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.
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
BZR – Belousov Zhabotinsky Reaction
Small hack into BZR – Belousov Zhabotinsky Reaction, a more continuous differentiated model
Agent Morphologies
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.
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.
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.
surface active tesselation 004
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
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
watch it fullscreen
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.
