Towards Sustainable Architecture: 3D Convolutional Neural Networks for Computational Fluid Dynamics Simulation and Reverse DesignWorkflow
arXiv:1912.02125
Abstract
We present a general and flexible approximation model for near real-time prediction of steady turbulent flow in a 3D domain based on residual Convolutional Neural Networks (CNNs). This approach can provide immediate feedback for real-time iterations at the early stage of architectural design. This work-flow is then reversed and offers a designer a tool that generates building volumes based on target wind flow.
NeurIPS Workshop on Machine Learning for Creativity and Design 3.0, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada