Méthodes de data mining pour l'analyse d'approximations numériques: Le cas de solutions asymptotiques des équations de Vlasov-Maxwell

Franck Assous; Joel Chaskalovic

doi:10.1016/j.crme.2010.07.001

Méthodes de data mining pour l'analyse d'approximations numériques: Le cas de solutions asymptotiques des équations de Vlasov-Maxwell

Translated title of the contribution: Data mining techniques for numerical approximations analysis: A test case of asymptotic solutions to the Vlasov-Maxwell equations

Franck Assous, Joel Chaskalovic

Department of Mathematics

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

We propose a novel approach that consists in using data mining techniques to perform a sensitivity analysis of approximate models. We give here the example of asymptotic solutions to Vlasov-Maxwell equations, obtained with a paraxial model for relativistic short beams. This new heuristic approach offers new potential applications to treat numerical solutions to mathematical models.

Translated title of the contribution	Data mining techniques for numerical approximations analysis: A test case of asymptotic solutions to the Vlasov-Maxwell equations
Original language	French
Pages (from-to)	305-310
Number of pages	6
Journal	Comptes Rendus - Mecanique
Volume	338
Issue number	6
DOIs	https://doi.org/10.1016/j.crme.2010.07.001
State	Published - Jun 2010

Keywords

Computer science
Data mining
Vlasov-Maxwell equations

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10.1016/j.crme.2010.07.001

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Méthodes de data mining pour l'analyse d'approximations numériques: Le cas de solutions asymptotiques des équations de Vlasov-Maxwell

Abstract

Keywords

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