# Towards Sustainable Architecture: 3D Convolutional Neural Networks for Computational Fluid Dynamics Simulation and Reverse DesignWorkflow

arXiv: https://arxiv.org/abs/1912.02125 · HTML: https://mlsanity.com/paper/1912.02125/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Positional Encoding Augmented GAN for the Assessment of Wind Flow for Pedestrian Comfort in Urban Areas](https://mlsanity.com/paper/2112.08447/) (2021)
- [Pedestrian Wind Factor Estimation in Complex Urban Environments](https://mlsanity.com/paper/2110.02443/) (2021)

## Abstract (arXiv)

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.