Deep Learning-Based Surrogate Modeling For Performance-Driven Generative Design Systems
Within the context of recent research to augment the design process with artificial intelligence (AI), this work contributes by introducing a new method. The proposed method automates the design environmental performance evaluation by developing a deep learning-based surrogate model to inform the early design stages. The project is aimed at automating performative design aspects, enabling designers to focus on creative design space exploration while retrieving real-time predictions of environmental metrics of evolving design options in generative systems. This shift from a simulation-based to a prediction-based approach liberates designers from having to conduct simulation and optimization procedures and allows for their native design abilities to be augmented. When introduced into design systems, AI strategies can improve existing protocols, and enable attaining environmentally conscious designs and achieve UN Sustainable Development Goal 11.
Keywords: Deep Learning; Artificial Intelligence; Surrogate Modeling; Automating Building Performance Simulation; Generative Design Systems; Sdg11