Visual exploration of patterns in multi-run time-varying multi-field simulation data using projected views

In the fields of science and engineering, it is common to run hundreds of simulations to investigate the dependence of the modeled process on various simulation and input the parameters. We propose a comprehensive approach for the visual analysis of such multi-run data to detect patterns and outliers. We use dimensionality reduction algorithms to generate a visual representation that exhibits the distribution of the simulation results under varying parameter settings. Each field (or even multi-field) of every time step and every simulation run is represented as a point in a 2D space, where the 2D layout conveys similarity of the scalar fields. Points corresponding to consecutive time steps of one run are connected by line segments, such that each simulation run is represented as a polyline. Consequently, the multi-run data are visually encoded as a set of polylines. Variations of hue, saturation, opacity, and shape allow for distinguishing groups of simulations and depicting various characteristics of runs. The user can interactively change these settings, while further interaction mechanisms allow for selection, refinement, zooming, requesting textual information, and brushing and linking to coordinated (or embedded) views of physical and attribute space visualizations. We apply our approach to two applications with significantly different data structure: a multi-run climate simulation over a 2D regular grid and a multi-run binary star evolution simulation with unstructured 3D particles evolving over time. We demonstrate the contribution and impact of our visualization method for the interactive visual analysis of the multi-run data by identifying meaningful groups of simulations, detecting global patterns, and finding interesting outliers