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Related Experiment Video

Updated: Nov 15, 2025

Using Generative Art to Convey Past and Future Climate Transitions
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Color-Patterns to Architecture Conversion through Conditional Generative Adversarial Networks.

Diego Navarro-Mateu1, Oriol Carrasco2, Pedro Cortes Nieves3

  • 1School of Architecture, Universitat Internacional de Catalunya, 08017 Barcelona, Spain.

Biomimetics (Basel, Switzerland)
|March 6, 2021
PubMed
Summary
This summary is machine-generated.

This study explores using gene regulatory patterns and machine learning to generate complex architectural designs. Conditional Generative Adversarial Networks (cGANs) are tested for coding architectural patterns into images and creating 2D representations.

Keywords:
architectureartificial intelligencecGANsgenerativemachine learningneuronal networkspatterns

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Area of Science:

  • Computational Design
  • Artificial Intelligence in Architecture
  • Generative Design

Background:

  • Complex realities can be simplified into recognizable patterns found in nature.
  • Architecture design consciously or unconsciously uses these patterns.
  • Evo-Devo gene regulators and Machine Learning offer new design/analysis workflows.

Purpose of the Study:

  • To test the feasibility of using conditional Generative Adversarial Networks (cGANs) for architectural pattern coding.
  • To explore generating complex phenotypic architecture from genotypic patterns.
  • To assess the potential of cGANs in creating novel architectural representations.

Main Methods:

  • Utilizing conditional Generative Adversarial Networks (cGANs) to code architectural patterns.
  • Translating color pattern-based images into 2D architectural representations.
  • Conducting scaled tests to validate the hypothesis and network flexibility.

Main Results:

  • Demonstrated feasibility of using cGANs for architectural pattern encoding.
  • Successfully translated image patterns into 2D architectural forms.
  • Assessed the adaptability of trained networks to novel, out-of-database cases.

Conclusions:

  • Conditional Generative Adversarial Networks (cGANs) show promise as a tool for architectural design and analysis.
  • This approach can generate complex architectural phenotypes from recognized patterns.
  • Further research can explore disruptive workflows combining Evo-Devo principles and AI.