학술논문

ASEVis: Visual Exploration of Active System Ensembles to Define Characteristic Measures
Document Type
Conference
Source
2022 IEEE Visualization and Visual Analytics (VIS) VIS Visualization and Visual Analytics (VIS), 2022 IEEE. :150-154 Oct, 2022
Subject
Computing and Processing
Atmospheric measurements
Visual analytics
Dynamics
Propulsion
Gain measurement
Particle measurements
Behavioral sciences
Language
ISSN
2771-9553
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.