Self-Adaptive Density Estimation of Particle Data - Archive ouverte HAL
Article Dans Une Revue SIAM Journal on Scientific Computing Année : 2016

Self-Adaptive Density Estimation of Particle Data

Hadrien Croubois
  • Fonction : Auteur
  • PersonId : 1010011
Nan Li
Esteban Rangel
  • Fonction : Auteur

Résumé

We present a study of density estimation, the conversion of discrete particle positions to a continuous field of particle density defined over a 3D 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.
Fichier principal
Vignette du fichier
peterka-siam-cse15-paper.pdf (2.77 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01824023 , version 1 (28-06-2018)

Identifiants

Citer

Tom Peterka, Hadrien Croubois, Nan Li, Esteban Rangel, Franck Cappello. Self-Adaptive Density Estimation of Particle Data. SIAM Journal on Scientific Computing, 2016, 38 (5), pp.S646 - S666. ⟨10.1137/15M1016308⟩. ⟨hal-01824023⟩

Collections

ENS-LYON INRIA UDL
54 Consultations
276 Téléchargements

Altmetric

Partager

More