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Meta Predictive Matter

computational texture synthesis

Abstract

As we stand on the threshold of a world defined in terms of digitally generated realities we need to consider more carefully than ever before the question of space and the nature of its reality (Woods,2002). We are observing a rapid change in the way space is being perceived and mediated, which is a direct consequence of advances in information and computational technologies. The data handling capacities of computers, the cutting edge analysis techniques and the custom developed software allow designers to address much more complex problems than ever before. As a result, a broader solution space can be explored, the designers gain access to massive data libraries and direct collaboration among different disciplines is facilitated. This requires a whole new set of design techniques, but, most importantly, new mind sets which enable algorithmic thinking and even a new level of cognition and understanding of space. Apart from mere geometrical representations, computational models become the new interfaces between different domains, where information science and mathematics respectively, play a crucial role. Models slowly become a new medium of experience, exploration and learning at the same time,revealing different levels of complexities.

In order to formulate a new design approach, in this context, there is a need for a paradigm shift towards a more participatory and open design system, where the designer’s intentions are informed by easily accessible data, relating to user behaviour and where design intuition is combined with analytical tools. Nowadays, architects can handle apart from non-Euclidean geometries, multi-dimensional data-sets and they can develop custom design systems.

Thus, this study suggests reconsidering the notion of design-thinking and speculates on novel ways of synthesising and producing architectural textures. It regards space as a scalar field of activities performed within it and uses as its reference system an abstraction of space, as it is experienced by its users. In this way, it promotes a methodology of generative texture formation by combining existing texture maps by means of computation. It particularly focuses on: a) gaining insight in the intractable design relationships that cannot be modeled using conventional associative methods and b) addressing both the possibilities in concept generation and the challenges in the translation from design to production.  

Design intentions and objectives

The project is built upon the idea that the activities performed in a specific space are mainly based on the user’s intuition within the available environmental context (light, sound etc). This intuitive human choice is suggesting a topological relationship of spaces and materials, making activities an abstraction of architectural space or indexes of any existing spatial setup.We suggest a method for texture synthesis, which can be informed by pre-srelected  or user defined texture maps. The research draws upon work done on the field of analysis of multi-dimensional datasets, like Principal Component Analysis (PCA)  and attempts to couple them with material research on functionally graded materials. The algorithmic definitions of the design tool were developed in Java programming language, while the experiments for the realization of the design outcomes were conducted using different additive and subtractive manufacturing technologies as well as a variety of materials (wood, resin, gypsum, ABS plastic) to test the performativity aspect of the generated textures.

The project was conducted from September 2012 till March 2013 at the Chair for Computer Aided Architectural Design at ITA of ETH Zurich, under the supervision of Professor Ludger Hovestadt, Manuel Kretzer and Hua Hao, by the students Styliani Azariadi, Evangelos Pantazis and Daniel Rohlek. The project is still under development and partially funded by the Ikea Foundation of Switzerland and by the LG Hausys/ Hi-MACS company.

  • Type:
    Computational modelling
  • Size/Scale:
    <50m
  • Location:
    ETH/ Zurich
  • Status:
    Completed
  • Team:
    Daniel Rohlek, Azariadi Stella

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