Self-adaptive density estimation of particle data

Tom Peterka, Hadrien Croubois, Nan Li, Esteban Rangel, Franck Cappello

Research output: Contribution to journalArticlepeer-review


We present a study of density estimation, the conversion of discrete particle positions to a continuous field of particle density defined over a three-dimensional Cartesian grid. The study features a methodology for evaluating the accuracy and performance of various density estimation methods, results of that evaluation for four density estimators, and a large-scale parallel algorithm for a self-adaptive method that computes a Voronoi tessellation as an intermediate step. We demonstrate the performance and scalability of our parallel algorithm on a supercomputer when estimating the density of 100 million particles over 500 billion grid points.

Original languageEnglish (US)
Pages (from-to)S646-S666
JournalSIAM Journal on Scientific Computing
Issue number5
StatePublished - 2016
Externally publishedYes


  • Cloud in cell
  • Density estimation
  • Nearest grid point
  • Smoothed particle hydrodynamics
  • Triangular shaped clouds
  • Voronoi tessellation

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics


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