  Machine learning is an emerging field of research in which complex data is
compacted through low order models that can faithfully reproduce the original
data with good accuracy. Machine learning of experimental and computational
data sets of fluid mechanics is valuable for design optimization, sensitivity
studies, fluid control, and as virtual laboratories in fluid mechanics
education. In this study, we have developed convolutional neural network (CNN)
models of two--dimensional laminar steady flow over single and tandem elliptic
cylinders with various cylinder aspect ratios, angles of attack, cylinder
separation, and flow Reynolds number. Multilayer perceptron neural networks
(MLPNN) are used to estimate the lift and drag coefficients with the above
parameters as input variables. The computational fluid dynamics data for
training and validation of the neural networks is generated by the commercial
software COMSOL using fine grids that gave grid--independent results. The paper
presents development and application of the neural networks, sample
demonstration at arbitrary sets of parameters, and sensitivity studies of the
parametric effects on lift and drag coefficients. Line plots of the variations
in lift and drag as functions of the governing parameters are also presented.
