  In this work we explore the advantages of end-to-end learning of multilayer
maps offered by feed forward neural-networks (FFNN) for learning and predicting
dynamics from transient fluid flow data.While machine learning in general
depends on data quality and quantity relative to the underlying dynamics of the
system, it is important for a given learning architecture to make the most of
this available information. To this end, we focus on data-driven problems where
there is a need to predict over reasonable time into the future with limited
data availability. Such function approximation or time series prediction is in
contrast to many applications of machine learning such as pattern recognition
and parameter estimation that leverage vast datasets. In this study, we
interpret the suite of recently popular data-driven learning approaches that
approximate the dynamics as Markov linear model in a higher-dimensional feature
space as a multilayer architecture similar to neural networks.
