Visual Analysis of Flow Features Using Information Theory

Over the past decades, scientific visualization has helped tremendously to easily generate meaningful representations of complicated data sets. However, with data correlated over many dimensions and millions of points, only few of the standard techniques are directly applicable. Unsteady multifield visualizations require effective reduction of the data to be displayed. From a huge amount of information, scientists must be able to extract the most informative parts. ??-machines, a concept based on information theory, can handle this task. They´re a finitestate machine representation of a system´s dynamics, which can be represented as a directed graph (see Figure 1). The nodes encode the local dynamics given as a spatiotemporal stochastic pattern, and the edges indicate the flow´s evolution. ??-machines consist of causal states and transitions between them. Several enhancements to the fundamental ??-machine representation can help users identify interesting time intervals, analyze the evolution of unusual local dynamics, and track features over time.