ASEVis: Visual Exploration of Active System Ensembles to Define Characteristic Measures

Marina Evers, Raphael Wittkowski, Lars Linsen

View presentation:2022-10-20T19:45:00ZGMT-0600Change your timezone on the schedule page
2022-10-20T19:45:00Z
Exemplar figure, described by caption below
The interactive analysis tool ASEVis: A programming interface (a) allows for the definition of time-dependent measures as well as aggregations. Aggregated measures are shown in a heatmap (b) while the aggregation over time is visualized in the timeplot (d). Detail visualizations for single ensemble members include animations (c), a line plot, and a scatter plot matrix.

Prerecorded Talk

The live footage of the talk, including the Q&A, can be viewed on the session page, Scientific Visualization, Ensembles, and Accessibility.

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Keywords

Physical & Environmental Sciences, Engineering, Mathematics ; Comparison and Similarity ; Coordinated and Multiple Views ; Application Motivated Visualization ; Task Abstractions & Application Domains ; Temporal Data

Abstract

Simulation ensembles are a common tool in physics for understanding how a model outcome depends on input parameters. We analyze an active particle system, where each particle can use energy from its surroundings to propel itself. A multi-dimensional feature vector containing all particles' motion information can describe the whole system at each time step. The system's behavior strongly depends on input parameters like the propulsion mechanism of the particles. To understand how the time-varying behavior depends on the input parameters, it is necessary to introduce new measures to quantify the difference of the dynamics of the ensemble members. We propose a tool that supports the interactive visual analysis of time-varying feature-vector ensembles. A core component of our tool allows for the interactive definition and refinement of new measures that can then be used to understand the system's behavior and compare the ensemble members. Different visualizations support the user in finding a characteristic measure for the system. By visualizing the user-defined measure, the user can then investigate the parameter dependencies and gain insights into the relationship between input parameters and simulation output.