Category: _ENGLISH


INTERVIEW : Ján Pernecký

Team members Aymeric and Rémi recently took part to the rese arch MEET UP and presented the approach of parametric design developed at Franck Boutté Consultants and through the MESH project. This was an opportunity to meet again Ján Pernecký, parametric designer and host of the event. Interview.

Founder or the “rese arch” initiative, teaching creative programming and robotic fabrication in several European universities and through workshops, researching generative processes in the context of art and architecture theory. Ján Pernecký studied architecture at Excessive III / Die Angewandte, Vienna; Academy of fine arts and Design, Bratislava; Faculty of Architecture STU, Bratislava and Arkitekturskolan KTH, Stockholm.

Maker of the Boid flocking library for Grasshopper® (2014).

Creator and curator of Asking Architecture - Slovak and Czech national pavilion at 13th architecture exhibition of la Biennale di Venezia.

Art digital - Image:
Art digital - Image:

Could you please introduce yourself in few words?

I’m an educated architect but I don’t practise anymore. I’m trying to make living through research in architecture and something I call non-applied research. I’m not into optimization of my own or other architects’ projects but rather into  conceptual thinking in architecture. Therefore I’ve created a platform , which gives the architects and thinkers an opportunity to develop, share and promote new ideas and notions.

Can you share your definition of computational design with us?

I personally think that there are three existing stages of computational design. I believe there is a fourth one that has not been practised yet but it should happen soon.

The first one is automation where you use the computer to do the heavy lifting for you. If you have an assignment that needs a lot of work, repetitive work, then the computer could help you a lot. I believe this is what BIM software does in general. This is what the computer has been doing ever since it started to be used but it doesn’t really bring any paradigm shift. It’s a conventional design approach, you just use the computer to do the hard work for you.

The second one would be parametric design which means the design is controlled by parameters which are fed into algorithms. The designer can act either on the algorithm or on the parameters to modify the final shape. In that process, the designer can evaluate the impact of parameters on the evaluation of its design. As the process can merge together a lot of different data and instructions the result can be surprising. But it doesn’t bring that much innovation because basically you have a presumption of what is going on. It’s not completely top-down but it is also still not bottom-up.

The last one that I can recognize as existing is a generative or emergent design where you can run a simulation that, by definition, is non-linear so you don’t know what the result is going to look like. You probably know roughly from which world it is coming but you don’t know exactly what the result will be when you are implementing some ideas into the design. This finally is a new paradigm. The design you get in the end is something that has not been done before or is not conventional because you don’t know accurately how it has been done. You are not designing the form itself but the tools that generate the form. As the generative process is non-linear you don’t know in advance what the result is going to look like but you very precisely know the forces generating the result. And the form emerges.

And there is the fourth stage that I think still doesn’t exist. I’d like to call it design by behaviour or designing the behaviour that generates the forms. The current emergent design is mainly bio-inspired: it takes existing behaviours and rules or rulesets from nature and it simulates them. That doesn’t make a lot of sense in architecture, but I believe certain architectural notions could be represented by autonomous agents. The agency could be totally architectural and you could design behaviours that form the final object of creation.

L’émergence est un concept philosophique formalisé au XIXe siècle et qui peut être grossièrement résumé par l'adage : « le tout possède parfois davantage de possibilités que la seule somme de ses parties ». Il existe en effet des entités dont les caractéristiques (constitutives) ne sont pas explicables à partir des caractéristiques de leurs parties. Ces caractéristiques apparaissent (émergent) du fait de l'organisation qui s'est créée spontanément.

D'un point de vue empirique, l'émergence est une façon de désigner l'apparition d'entités complexes irréductibles. Ce concept est illustré en sociologie par Emile Durkheim et Pierre Bourdieu, l’utilisant d’un point de vue holistique pour décrire l’émergence d’un ordre de faits irréductible aux parties du système et à leurs interactions [ 1 ].

Le concept est également mobilisé en neurosciences et sciences cognitives dans l’analyse des rapports entre cerveau et esprit. En architecture, urbanisme, design, la notion d’émergence est intimement liée à l’utilisation d’outils issus de progrès associés à l’intelligence artificielle comme les automates cellulaires, les réseaux neuronaux…

What are the parameters you deal with? Do you integrate environmental issues?

I made a couple of projects many years ago that were completely parametric and based on big data. I was trying to take all thinkable aspects into account: circulation, programmatic function, phenomenological phenomenon and perception of the final design because these were the only things I could imagine quantifying at that time. Today I would probably use a different sample set and pay more attention to the relative ranking of solutions than to the absolute value of the parameters or evaluations.

How does computational design influence collaboration between the different parties of your projects?

I see the amazing capacities of crowd-sourcing and crowd-designing, but I also see the risks. It allows you to involve everybody thinkable and to find a result that is based on everybody’s wishes and needs and expertise. You probably can find an output that in a certain sense optimum but is average. You level things up or down and what you end up with is really just mediocre. Something that cannot be extreme in a good sense. I can’t imagine that anything surprising will come up or come out of the collaborative process when too many parties are involved.

Is a computational approach not leading to a new form of automation that focuses on efficacy instead of quality, calculation rather than experience?

Some things are hard to quantify or it’s hard to imagine how to quantify them. But you don’t always need to quantify them. Basically, what you need, is a relative ranking of the performances. If you are able to put it on such a scale, then it’s enough to feed a genetic algorithm for example. It will take the most successful part of generated designs and then breed another generation of designs which is probably going to be better and so on.

To understand how to rank your solutions, you can now use neural networks. For example, you want to quantify the beauty of your design. Of course, you can give it to one thousand people and each one will select the most beautiful output. From this data, the network works out a way of evaluating your design and you can use it in your genetic algorithm.

There is a risk of misinterpreting big data though. Any data or information can be read right or wrong, by which I mean you don’t always read it the way it was meant to be read or the way it makes the most sense to be read. But on the other hand also the misreading could be interesting…

Un réseau de neurones artificiels (artificial neural network) est un modèle de calcul dont la conception est très schématiquement inspirée du fonctionnement des neurones biologiques. Les réseaux de neurones, en tant que systèmes capables d'apprendre, mettent en œuvre le principe de l'induction, c’est-à-dire l'apprentissage par l'expérience. Ils permettent notamment d’approximer des fonctions mathématiques inconnues et dépendant d’un grand nombre de variables ou de faire une modélisation accélérée d’une fonction connue mais très complexe à calculer avec exactitude.

Les réseaux de neurones sont généralement optimisés par des méthodes d’apprentissage de type probabiliste, en particulier bayésien. Ils sont placés d’une part dans la famille des applications statistiques, qu’ils enrichissent avec un ensemble de paradigmes permettant de créer des classifications rapides (réseaux de Kohonen en particulier), et d’autre part dans la famille des méthodes de l’intelligence artificielle auxquelles ils fournissent un mécanisme perceptif indépendant des idées propres de l'implémenteur, et fournissant des informations d'entrée au raisonnement logique formel (voir "Deep Learning").


Coming soon : MESH at the rese arch MEETUP

Tonight at Volumes Coworking will take place the lecture BIM vs. Computational design, towards a convergence featuring a panel of quality speakers. Find out more here.

MESH will also participate to the Datascape FEST, as Aymeric and Rémi will present their last projects and research at Rese Arch MEETUP, one of the closing events of the Datascapes FEST, 04th april 2018. More info here.

See you soon !


INTERVIEW – Daniel Bolojan

Founder of Nonstandardstudio [ 1 ], focused on development of innovative design methodologies and strategies, enacted through medium of computation, towards urbanism, architecture, design.

Junior Associate at Coop-himmelb(l)au in Vienna;

PhD Research Fellow at the University of Applied Arts Vienna, under the supervision of Patrik Schumacher, and Lecturer at - Institute of Structure and Design -  Innsbruck University.

Images générées par un processus multiagent. ©Nonstandardstudio
Images générées par un processus multiagent. ©Nonstandardstudio
Images générées par un processus multiagent. ©Nonstandardstudio
Images générées par un processus multiagent. ©Nonstandardstudio

Could you please introduce yourself in few words?

Hello, I’m Daniel Bolojan, and I’m the founder of Non-Standard Studio. My main interests lie in creating complexly interrelated autopoietic systems, similar to natural systems, with subsystems capable of increased awareness, adaptability, towards all their components and towards their environment.

Can you share your definition of computational design with us?

Computational design involves that the design intent is set through a set of algorithmic instructions, of rules, a logic. Advantages of this approach are the designer's abitlity to access DNA source of structural and material behaviors, or of collective behaviors at multiple scales. These advantages translate into a more embedded design process where constraints and inputs of fabrication, social interaction, navigation, or environmental constraints are part and are driving forces of the design.

My work revolves around generative design and multi-agent algorithms. That is, understood in the studio’s work, a design process aiming at modeling the proto conditions of a system.

On appelle système multi-agent (SMA), un système composé d’un ensemble d’agents qui sont des entités réelles ou virtuelles, dont le comportement est autonome, évoluant dans un environnement, qu’ils sont capables de percevoir, sur lequel ils sont capable d’agir, et d’interagir entre eux.

Yves Demazeau, Populations and organizations in open multi-agent systems


Objet de longue date de recherches en intelligence artificielle distribuée, les systèmes multi-agents forment un type intéressant de modélisation de sociétés, et ont à ce titre des champs d'application larges, allant jusqu'aux sciences humaines.

How do you evaluate the quality of the result of this generative process?

First I have to point out that generative design follows a cycle through problem specification, design generation and design evaluation. Usually, while setting up the system, the designer can interactively control its behavior by specifying the design intent that needs to be embedded in the system. This design intent can represent constraints that must be met, desirable underlying logic it should follow, or characteristics the system should have. All these different types of design intents will have for sure different implications for how the generative system will work.

With regards to the evaluation of generative processes we have to keep in mind the two aspects of design evaluations.

On the one hand, the design intent's main features are embedded in the generative process which can directly result into possible evaluations of different quantifiable properties, characteristics of the design. Most of my work revolves around stigmergic models, composed of two parts: agents and the environment. The agents act according to pheromone trails stored into grid-like nodes that form the environment. The agents modify the environment  and get their behaviours modified according to the new modified environment. Behavioral characteristics are easily quantifiable and therefore could be subject to evaluations of the outcome by the system itself (e.g. find if an optimum path was found). In this case we can consider this a form of “system evaluation”.

On the other hand, however, there is a requirement for evaluations of emergent features of designs, that address as much technical reasons as aesthetic reasons. These evaluations are more difficult to compute from generative system’s design outcome, as they depend most of the time on subtle details of design aesthetics and context. For this type of evaluations there has to be a form of “designer evaluation”.

Could you explain the process you developed within “Ubiquitous Urbanism”?

Ubiquitous urbanism is a project that I did when I was studying in die Angewandte[ 2 ]/ Studio Zaha Hadid.

In the ecology of self-organized systems, there will always be different types of agent systems, with different behaviors and desires that will sense or modify their environment in a different way, based on their own characteristics.

At the urban scale, each main functional type (office, retail, recreation, housing, culture, transportation), had its own agent based system with its own functional type specifics/constraints. Series of interdependent differences, and correlation of resulting differentiated series, emerge according to the resulting stigmergy ecology, responsible for degrees of differentiation, different affiliation rules, and degrees of correlations.

The main idea of the project was to extract and use local rules of physical and visual connectivity as a bottom-up strategy to generate growth algorithms that will regulate spatial formations. These neighboring relations, based on physical and visual connectivity, don’t only allow a local neighboring negotiation, but also a global neighboring negotiation. The result is a coherent field not only on a local scale, but also on a global scale.

Ubiquitous urbanism (2013). ©Studio Zaha Hadid
Ubiquitous urbanism (2013). ©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 a research of individual urban systems, where on the example of working environment, we have addressed problems, needs and desires of corporative field."

Do you think your approach could be related to traditional urbanism?

If we mean by “traditional urbanism” the type of emergence that can be found in traditional urban formations as favelas, then my approach is very much so in line with traditional urban formations. As simple rules of emergence, such as neighboring rules, operate at an individual level and interact to give rise to the emergence of self-organization, a behavior emerges from these interactions and it’s something greater than the sum of its part.