While is often assumed that adaptation to a complex set of phenomena requires a complex control mechanism, Adaptive Fa[CA]de suggests a simpler control mechanism in terms of independent units, yet more contextual to its environment. Rather than being a constantly moving structure which would waste energy and lead to potential breakdown, the façade is trained to anticipate its own future behaviour and therefore move less to adapt. To achieve the above, the inherent structural and performative characteristics of CA are used as means to obtain optimum light levels to the interior of the building.
The façade is composed by a finite grid of panels that switch through a number of possible states translated in tilting angles. The hypothesis is that the facade can be trained by artificial Neural Networks and use the complexity of CA to take minimum analog input of the environment, translate it to digital and – through optimisation – back to analog in the form of different CA patterns.
Due to CA’s emergent nature, the information is distributed throughout the grid of panels. Learning the irregularity of both the complex embedded structural attributes of CA and external light conditions, the system can then propagate successful patterns. Complexity here is used as an interface between different layers of information and suggests an alternative way to deal with the complexity found in most architectural configurations
‘Adaptive Fa[ca]de’ explores the functional possibilities and performative characteristics of cellular automata (CA). In addition to the unique emergent behaviour of CA, a neural network enables a further computational layer to evolve CA behaviour to the context of its surrounding environment. Building upon the early work of Conway’s ‘Game of life’ and Stephen Wolfram’s extensive research on the wider implementation of CA, ‘Adaptive Fa[ca]de’ becomes a living adapting skin, constantly training itself from the history of its own errors and achievements.’