Visual Analysis of Multi-Parameter Distributions across Ensembles of 3D Fields

Alexander Kumpf, Josef Stumpfegger, Patrick Härtl, Rüdiger Westermann

View presentation:2021-10-27T14:15:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
We present a workflow and high-performance system for analysing the representativeness of a selected multi-parameter distribution in a simulation ensemble. An automatic refinement in multi-parameter space (top row) computes tight hyper-boxes in multi-parameter space that are applied to all ensemble members.(d) A novel variant of violin plots enables the effective visualization of multi-parameter distributions and comparison between different members. (e) A linked 3D view shows the locations of data points falling within the selected multi-parameter distribution.
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Ensemble visualization, multi-parameter visualization, 3D rendering, distribution comparison, parallel coordinate


For an ensemble of 3D multi-parameter fields, we present a visual analytics workflow to analyse whether and which parts of a selected multi-parameter distribution is present in all ensemble members. Supported by a parallel coordinate plot, a multi-parameter brush is applied to all ensemble members to select data points with similar multi-parameter distribution. By a combination of spatial sub-division and a covariance analysis of partitioned sub-sets of data points, a tight partition in multi-parameter space with reduced number of selected data points is obtained. To assess the representativeness of the selected multi-parameter distribution across the ensemble, we propose a novel extension of violin plots that can show multiple parameter distributions simultaneously. We investigate the visual design that effectively conveys (dis-)similarities in multi-parameter distributions, and demonstrate that users can quickly comprehend parameter-specific differences regarding distribution shape and representativeness from a side-by-side view of these plots. In a 3D spatial view, users can analyse and compare the spatial distribution of selected data points in different ensemble members via interval-based isosurface raycasting. In two real-world application cases we show how our approach is used to analyse the multi-parameter distributions across an ensemble of 3D fields.