کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
520686 867732 2011 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Data mining techniques for scientific computing: Application to asymptotic paraxial approximations to model ultrarelativistic particles
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
Data mining techniques for scientific computing: Application to asymptotic paraxial approximations to model ultrarelativistic particles
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Computational Physics - Volume 230, Issue 12, 1 June 2011, Pages 4811–4827
نویسندگان
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