I think there could be a combined approach where you use the fast neural net and the slow CFD as you state, but then every result you get from the CFD you add to the training data of the neural net and do a few more steps of training.

That way, the neural net gets more accurate specifically in the area of your design.

You could imagine a developers workstation where a GUI tool is letting them design stuff, and the neural net is giving them instant answers (and/or showing gradients to show how to improve stuff), and slow CFD runs are running in the background so whenever they go to lunch and another cfd run completes the results all get more accurate and detailled.

Kinda similar to the way many graphics packages have 'draft' quality 3d rendering, but then when idle for a bit will improve the render quality.