PROJECT TITLE :

Streamline Variability Plots for Characterizing the Uncertainty in Vector Field Ensembles

ABSTRACT:

We present a brand new technique to visualize from an ensemble of flow fields the statistical properties of streamlines passing through a selected location. We have a tendency to use principal component analysis to rework the set of streamlines into a low-dimensional Euclidean space. During this house the streamlines are clustered into major trends, and each cluster is in turn approximated by a multivariate Gaussian distribution. This yields a probabilistic mixture model for the streamline distribution, from that confidence regions can be derived in which the streamlines are possibly to reside. This can be achieved by transforming the Gaussian random distributions from the low-dimensional Euclidean house into a streamline distribution that follows the statistical model, and by visualizing confidence regions during this distribution via iso-contours. We tend to additional build use of the principal part illustration to introduce a brand new concept of streamline-median, based on existing median concepts in multidimensional Euclidean spaces. We tend to demonstrate the potential of our technique in an exceedingly range of real-world examples, and we have a tendency to compare our results to various clustering approaches for particle trajectories also curve boxplots.