论文标题

ASEVIS:视觉探索活动系统集合以定义特征度量

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

论文作者

Evers, Marina, Wittkowski, Raphael, Linsen, Lars

论文摘要

仿真集合是物理中的常见工具,用于了解模型结果如何取决于输入参数。我们分析了一个活跃的粒子系统,每个粒子都可以利用周围环境中的能量来推动本身。包含所有粒子运动信息的多维特征向量可以在每个时间步骤中描述整个系统。系统的行为在很大程度上取决于输入参数,例如粒子的推进机理。为了了解时间变化的行为如何取决于输入参数,有必要引入新的措施来量化集合成员的动力学差异。我们提出了一种支持时变特征向量集合的交互式视觉分析的工具。我们工具的核心组成部分允许对新措施进行交互定义和完善,然后可以使用这些措施来理解系统的行为并比较集合成员。不同的可视化支持用户为系统找到特征度量。通过可视化用户定义的度量,用户可以研究参数依赖项并获得对输入参数和仿真输出之间关系的见解。

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.

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