Data mining methods for performance evaluations to asymptotic numerical models

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.