  We demonstrate several techniques to encourage practical uses of neural
networks for fluid flow estimation. In the present paper, three perspectives
which are remaining challenges for applications of machine learning to fluid
dynamics are considered: 1. interpretability of machine-learned results, 2.
bulking out of training data, and 3. generalizability of neural networks. For
the interpretability, we first demonstrate two methods to observe the internal
procedure of neural networks, i.e., visualization of hidden layers and
application of gradient-weighted class activation mapping (Grad-CAM), applied
to canonical fluid flow estimation problems -- $(1)$ drag coefficient
estimation of a cylinder wake and $(2)$ velocity estimation from particle
images. It is exemplified that both approaches can successfully tell us
evidences of the great capability of machine learning-based estimations. We
then utilize some techniques to bulk out training data for super-resolution
analysis and temporal prediction for cylinder wake and NOAA sea surface
temperature data to demonstrate that sufficient training of neural networks
with limited amount of training data can be achieved for fluid flow problems.
The generalizability of machine learning model is also discussed by accounting
for the perspectives of inter/extrapolation of training data, considering
super-resolution of wakes behind two parallel cylinders. We find that various
flow patterns generated by complex interaction between two cylinders can be
reconstructed well, even for the test configurations regarding the distance
factor. The present paper can be a significant step toward practical uses of
neural networks for both laminar and turbulent flow problems.
