  Reconstruction of flow field from real sparse data by a physics-oriented
approach is a current challenge for fluid scientists in the AI community. The
problem includes feature recognition and implementation of AI algorithms that
link data to a physical feature space in order to produce reconstructed data.
The present article applies machine learning approach to study contribution of
different flow-based features with practical fluid mechanics applications for
reconstruction of the missing data of turbomachinery PIV measurements. Support
vector regression (SVR) and multi-layer perceptron (MLP) are selected as two
robust regressors capable of modelling non-linear fluid flow phenomena. The
proposed flow-based features are optimally scaled and filtered to extract the
best configuration. In addition to conventional data-based validation of the
regressors, a metric is proposed that reflects mass conservation law as an
important requirement for a physical flow reproduction. For a velocity field
including 25% of clustered missing data, the reconstruction accuracy achieved
by SVR in terms of R2-score is as high as 0.993 for the in-plane velocity
vectors in comparison with that obtained by MLP which is up to 0.981. In terms
of mass conservation metric, the SVR model by R2-score up to 0.96 is
considerably more accurate than the MLP estimator. For extremely sparse data
with a gappiness of 75%, vector and contour plots from SVR and MLP were
consistent with those of the original field.
