Name
Title | ||
|---|---|---|
Data mining techniques for scientific computing: Application to asymptotic paraxial approximations to model ultrarelativistic particles |
Abstract | ||
|---|---|---|
We propose a new approach that consists in using data mining techniques for scientific computing. Indeed, data mining has proved to be efficient in other contexts which deal with huge data like in biology, medicine, marketing, advertising and communications. Our aim, here, is to deal with the important problem of the exploitation of the results produced by any numerical method. Indeed, more and more data are created today by numerical simulations. Thus, it seems necessary to look at efficient tools to analyze them. In this work, we focus our presentation to a test case dedicated to an asymptotic paraxial approximation to model ultrarelativistic particles. Our method directly deals with numerical results of simulations and try to understand what each order of the asymptotic expansion brings to the simulation results over what could be obtained by other lower-order or less accurate means. This new heuristic approach offers new potential applications to treat numerical solutions to mathematical models. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.jcp.2011.03.005 | J. Comput. Physics |
Keywords | Field | DocType |
vlasov–maxwell equations,data mining,new potential application,ultrarelativistic particle,asymptotic methods,new approach,data mining technique,numerical method,new heuristic approach,paraxial approximation,huge data,scientific computing,numerical result,numerical simulation,numerical solution,asymptotic expansion,maxwell equation,mathematical model | Data mining,Mathematical optimization,Heuristic,Paraxial approximation,Asymptotic expansion,Computational science,Mathematical model,Numerical analysis,Mathematics,Maxwell's equations | Journal |
Volume | Issue | ISSN |
230 | 12 | Journal of Computational Physics |
Citations | PageRank | References |
5 | 1.20 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Franck Assous | 1 | 13 | 9.38 |
| Joël Chaskalovic | 2 | 5 | 4.25 |