  Robust feature extraction is an integral part of scientific visualization. In
unsteady vector field analysis, researchers recently directed their attention
towards the computation of near-steady reference frames for vortex extraction,
which is a numerically challenging endeavor. In this paper, we utilize a
convolutional neural network to combine two steps of the visualization pipeline
in an end-to-end manner: the filtering and the feature extraction. We use
neural networks for the extraction of a steady reference frame for a given
unsteady 2D vector field. By conditioning the neural network to noisy inputs
and resampling artifacts, we obtain numerically stabler results than existing
optimization-based approaches. Supervised deep learning typically requires a
large amount of training data. Thus, our second contribution is the creation of
a vector field benchmark data set, which is generally useful for any local deep
learning-based feature extraction. Based on Vatistas velocity profile, we
formulate a parametric vector field mixture model that we parameterize based on
numerically-computed example vector fields in near-steady reference frames.
Given the parametric model, we can efficiently synthesize thousands of vector
fields that serve as input to our deep learning architecture. The proposed
network is evaluated on an unseen numerical fluid flow simulation.
