Visualizing Time-Varying Particle Flows with Diffusion Geometry

TL;DR

This paper introduces a diffusion geometry-based method for visualizing and analyzing separation and clustering in time-varying particle flow data, addressing challenges of unstructured, sparse sampling.

Contribution

It proposes a novel particle similarity measure using diffusion geometry, enabling continuous exploration of flow structures in complex, time-dependent particle datasets.

Findings

Effective in 2D and 3D flow datasets

Robust to sampling imperfections

Facilitates user exploration of flow structures

Abstract

The tasks of identifying separation structures and clusters in flow data are fundamental to flow visualization. Significant work has been devoted to these tasks in flow represented by vector fields, but there are unique challenges in addressing these tasks for time-varying particle data. The unstructured nature of particle data, nonuniform and sparse sampling, and the inability to access arbitrary particles in space-time make it difficult to define separation and clustering for particle data. We observe that weaker notions of separation and clustering through continuous measures of these structures are meaningful when coupled with user exploration. We achieve this goal by defining a measure of particle similarity between pairs of particles. More specifically, separation occurs when spatially-localized particles are dissimilar, while clustering is characterized by sets of particles that are similar to one another. To be robust to imperfections in sampling we use diffusion geometry to compute particle similarity. Diffusion geometry is parameterized by a scale that allows a user to explore separation and clustering in a continuous manner. We illustrate the benefits of our technique on a variety of 2D and 3D flow datasets, from particles integrated in fluid simulations based on time-varying vector fields, to particle-based simulations in astrophysics.