Diseño generativo en Arquitectura. Una reinterpretación de la ETSAM
Bibliographic record
Abstract
Las nuevas tecnologías avanzan de forma cada vez más rápida, y el campo de la arquitectura no es una excepción. La explosión de las inteligencias artificiales ha llegado, y con ello, es ahora el momento en el que todos los campos del conocimiento se encuentran buscando cómo aplicarlas en su sector. El diseño generativo aplicado a la arquitectura abre un abanico de posibilidades infinito al diseñador, desde la fase de conceptualización, exploración de ideas y su materialización. Por ejemplo, en el diseño de las nuevas oficinas de Autodesk en Toronto. La capacidad de cálculo de los ordenadores fue usada para generar, evaluar y evolucionar miles de alternativas de diseño a partir de una serie de objetivos y restricciones. Este estudio comienza desde un punto de vista teórico, descubriendo y entendiendo cómo funciona el diseño generativo, para luego aplicarlo en el caso práctico, acabando de entender cómo puede influir a la hora de enriquecer un proyecto. El trabajo busca ofrecer una perspectiva sobre el potencial del diseño generativo, desde los conceptos hasta aplicaciones prácticas, así como evaluar su viabilidad como una alternativa innovadora al flujo de trabajo tradicional que se enseña en las escuelas de Arquitectura y por extensión en el ámbito profesional. The rapid advancement of new technologies extends across every field, and architecture is no exception. The surge in artificial intelligence has arrived, prompting all realms of knowledge to explore how to apply it within their domains. Generative design, when applied to architecture, opens an infinite array of possibilities for designers, spanning from conceptualization and idea exploration to its materialization. For instance, in the design of Autodesk’s new offices in Toronto, computer computational capabilities were utilized to generate, evaluate, and evolve thousands of design alternatives based on a set of objectives and constraints. This study begins from a theoretical perspective, unraveling and comprehending the workings of generative design, and then applying it in practical cases to grasp its potential impact on enriching a project. The objective of this work is to provide insight into the potential of generative design, spanning from fundamental concepts to practical applications, and to assess its viability as an innovative alternative to the traditional workflow taught in architectural schools and, by extension, in the professional world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".