In the world of computational design, the term "cybernetics" might conjure images of sci-fi and cutting-edge AI. However, within the academic context, cybernetics refers to systems that detect and measure changes, and subsequently adjust based on those changes to optimize certain attributes, creating equilibrium or homeostasis. This can range from simple mechanisms, like a thermostat, to complex systems, like an automatic guidance system for an interstellar spaceship, or the concept of the invisible hand in capitalist economics.
Humans, like all lifeforms, exhibit varying levels of cybernetic complexity. Our bodies regulate ATP in muscles, control pupil diameter, and trigger hunger or evacuation signals. Even simple cognitive tasks, such as grabbing an object or, for example, typing an article, involve a range of cybernetic loops. There are cases where human feedback systems become dysregulated, turning these simple tasks into tremendous feats of concentration as each subtle adjustment swings farther out of control with each update. You can also experience a similar experience when you have trouble completing actions in dreams that require feedback, such as running or punching.
Architectural systems, even static ones, have great potential for improvement through a cybernetic approach to optimization. By simulating and measuring environmental and physical characteristics such as albedo, visibility, incident solar radiation, finite element analysis, and sound absorption, an architectural design can optimize a multi-dimensional latent space. This process solves complex multi-dimensional gradient descent problems, resulting in a series of synergetic solutions.
Reflecting on a personal anecdote, I once participated in a physical fitness competition during college. Our team didn't win first place in any single event but consistently placed high across the board. To our surprise, this consistency earned us the overall first prize, as our aggregate performance outshone teams with individual wins but less consistent results.
Multidimensional optimization in computational design is similar. Exploring a vast set of possible configurations would be impractical through brute force sampling. Instead, intelligent sampling techniques or gradient descent algorithms can be employed to navigate the problem space efficiently, finding optimal solutions that balance various criteria.
Imagine a two-dimensional gradient descent graph where the third dimension (height) represents magnitude. If x represents total incident solar radiation and y represents the total volume of a building, the optimal solution lies at the lowest point on the graph. This balance might be between a large volume for maximizing space and a normal angle vector optimizing solar radiation. Some combination of these factors will yield the best overall design solution according to the defined ideal parameters (which itself is enough for a whole discussion).
Neural networks have excelled in recent years at generating and exploring multidimensional latent spaces. Combining genetic algorithms with neural network models can create a feedback loop, enhancing the ability to solve complex socio-spatial relationships. This integration can lead to more meaningful, evidence-based solutions, democratizing beauty and comfort in architectural designs.
In conclusion, the application of cybernetics in computational design offers a promising avenue for creating optimized, harmonious architectural solutions. By leveraging feedback loops and advanced algorithms, we can push the boundaries of design, making our built environment more adaptive, efficient, and inspiring.
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