  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.
