Data mining methods for performance evaluations to asymptotic numerical models

Franck Assous, Joel Chaskalovic

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper proposed a new approach based on data mining to evaluate the efficiency of numerical asymptotic models. Indeed, data mining has proved to be an efficient tool of analysis in several domains. In this work, we first derive an asymptotic paraxial approximation to model ultrarelativistic particles. Then, we use data mining methods that directly deal with numerical results of simulations, to understand what each order of the asymptotic expansion brings to the simulation results. This new approach offers the possibility to understand, on the numerical results themselves, the precision level of a numercial asymptotic model.

Original languageEnglish
Pages (from-to)518-527
Number of pages10
JournalProcedia Computer Science
Volume4
DOIs
StatePublished - 2011
Externally publishedYes
Event11th International Conference on Computational Science, ICCS 2011 - Singapore, Singapore
Duration: 1 Jun 20113 Jun 2011

Keywords

  • Asymptotic methods
  • Data mining
  • Paraxial approximation
  • Vlasov-maxwell equations

Fingerprint

Dive into the research topics of 'Data mining methods for performance evaluations to asymptotic numerical models'. Together they form a unique fingerprint.

Cite this