Navarro-Mateu Diego, Carrasco Oriol, Cortes Nieves Pedro
School of Architecture, Universitat Internacional de Catalunya, 08017 Barcelona, Spain.
Institute for Advanced Architecture of Catalonia, 08005 Barcelona, Spain.
Biomimetics (Basel). 2021 Feb 17;6(1):16. doi: 10.3390/biomimetics6010016.
Often an apparent complex reality can be extrapolated into certain patterns that in turn are evidenced in natural behaviors (whether biological, chemical or physical). The Architecture Design field has manifested these patterns as a conscious (inspired designs) or unconscious manner (emerging organizations). If such patterns exist and can be recognized, can we therefore use them as genotypic DNA? Can we be capable of generating a phenotypic architecture that is manifestly more complex than the original pattern? Recent developments in the field of Evo-Devo around gene regulators patterns or the explosive development of Machine Learning tools could be combined to set the basis for developing new, disruptive workflows for both design and analysis. This study will test the feasibility of using conditional Generative Adversarial Networks (cGANs) as a tool for coding architecture into color pattern-based images and translating them into 2D architectural representations. A series of scaled tests are performed to check the feasibility of the hypothesis. A second test assesses the flexibility of the trained neural networks against cases outside the database.
通常，一个看似复杂的现实可以外推到某些模式中，而这些模式又会在自然行为（无论是生物、化学还是物理行为）中得到体现。建筑设计领域已将这些模式以有意识的方式（灵感设计）或无意识的方式（新兴组织）表现出来。如果存在这样的模式并且能够被识别，那么我们能否将它们用作基因型DNA呢？我们能否生成一种明显比原始模式更复杂的表型建筑呢？进化发育生物学领域中围绕基因调控模式的最新进展或机器学习工具的迅猛发展，可以结合起来为开发新的、具有颠覆性的设计和分析工作流程奠定基础。本研究将测试使用条件生成对抗网络（cGANs）作为一种工具的可行性，该工具可将建筑编码为基于颜色模式的图像，并将其转换为二维建筑表示。进行了一系列比例测试以检验该假设的可行性。第二项测试评估训练后的神经网络在数据库之外的情况下的灵活性。
