{"title":"Localized Evaluation for Constructing Discrete Vector Fields","authors":"Tanner Finken, Julien Tierny, Joshua A Levine","doi":"arxiv-2408.04769","DOIUrl":null,"url":null,"abstract":"Topological abstractions offer a method to summarize the behavior of vector\nfields but computing them robustly can be challenging due to numerical\nprecision issues. One alternative is to represent the vector field using a\ndiscrete approach, which constructs a collection of pairs of simplices in the\ninput mesh that satisfies criteria introduced by Forman's discrete Morse\ntheory. While numerous approaches exist to compute pairs in the restricted case\nof the gradient of a scalar field, state-of-the-art algorithms for the general\ncase of vector fields require expensive optimization procedures. This paper\nintroduces a fast, novel approach for pairing simplices of two-dimensional,\ntriangulated vector fields that do not vary in time. The key insight of our\napproach is that we can employ a local evaluation, inspired by the approach\nused to construct a discrete gradient field, where every simplex in a mesh is\nconsidered by no more than one of its vertices. Specifically, we observe that\nfor any edge in the input mesh, we can uniquely assign an outward direction of\nflow. We can further expand this consistent notion of outward flow at each\nvertex, which corresponds to the concept of a downhill flow in the case of\nscalar fields. Working with outward flow enables a linear-time algorithm that\nprocesses the (outward) neighborhoods of each vertex one-by-one, similar to the\napproach used for scalar fields. We couple our approach to constructing\ndiscrete vector fields with a method to extract, simplify, and visualize\ntopological features. Empirical results on analytic and simulation data\ndemonstrate drastic improvements in running time, produce features similar to\nthe current state-of-the-art, and show the application of simplification to\nlarge, complex flows.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"71 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Graphics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.04769","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Topological abstractions offer a method to summarize the behavior of vector
fields but computing them robustly can be challenging due to numerical
precision issues. One alternative is to represent the vector field using a
discrete approach, which constructs a collection of pairs of simplices in the
input mesh that satisfies criteria introduced by Forman's discrete Morse
theory. While numerous approaches exist to compute pairs in the restricted case
of the gradient of a scalar field, state-of-the-art algorithms for the general
case of vector fields require expensive optimization procedures. This paper
introduces a fast, novel approach for pairing simplices of two-dimensional,
triangulated vector fields that do not vary in time. The key insight of our
approach is that we can employ a local evaluation, inspired by the approach
used to construct a discrete gradient field, where every simplex in a mesh is
considered by no more than one of its vertices. Specifically, we observe that
for any edge in the input mesh, we can uniquely assign an outward direction of
flow. We can further expand this consistent notion of outward flow at each
vertex, which corresponds to the concept of a downhill flow in the case of
scalar fields. Working with outward flow enables a linear-time algorithm that
processes the (outward) neighborhoods of each vertex one-by-one, similar to the
approach used for scalar fields. We couple our approach to constructing
discrete vector fields with a method to extract, simplify, and visualize
topological features. Empirical results on analytic and simulation data
demonstrate drastic improvements in running time, produce features similar to
the current state-of-the-art, and show the application of simplification to
large, complex flows.