The following articles, describe the process that have been carried out to support, refute or validate design hypothesis. The articles also described the cause and effect in the outcome design after a parameter have been modified. These experiments vary in scale and objectives, but all of them rely in empirical experimentation, design, research and analysis.
I have blend technology research and architectural design for over 15 years. Technology is the driven tool for innovation, and the synergies between both concepts are in perpetual motion. It's necessary technology to innovate and innovation to develop new technologies. Every company should develop their own technology to offer unique and competitive products. And most important to adapt quickly and efficiently to a new business model.
The following slides define the fields I have developed during these years in applied technology and research. (The definitions express the way how I understand these fields).
It's the process that represents all possible design solutions for an specific problem. The process is compose by geometric and arithmetic rules, which are explicit to the design. Nonetheless, the design solutions, like geometry, colors, etc. are implicit to the design.
The rules are explicit to design, because you can see them. If a rule it's not visible in the process (algorithm), it means does not exist. In the other hand, the geometry or a part of it might not be visible, but exist. For example, a surface is defined to host a cube, but only the cube is visible. Therefore the geometry, the surface, in this case, is implicit to design.
Taking a generative design as base root, a Design Optimization implements a mathematical model to generate high-quality design solutions for an specific environment. The environment determine the design constraints and requirements, and it's essential to define the fitness in every design solution.
Genetic Algorithms, implies the Darwin Natural Selection theory as a mathematical model to explore vast and complex search spaces, to return high-quality design solutions. Genetic Algorithm Library
Machine Learning Techniques
Machine Learning is a field of Artificial Intelligence, the same as Genetic Algorithms. M.L. techniques group a series of mathematical models that can reveal hidden patterns from the environment observation. The environment can be any data set: images, videos, sounds, texts, numbers or the combination of these. The models are able to learn and generalize the information from the environment to predict, classify, perform tasks, etc.
Common ML techniques are: Supervised learning, Unsupervised learning, Reinforcement learning and Neuronal networks (deep learning).