Discovering spectral methods
Article REF: AF520 V1

Discovering spectral methods

Authors : Christine BERNARDI, Yvon MADAY

Publication date: April 10, 2013 | Lire en français

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Overview

ABSTRACT

Spectral methods, as discretization techniques of equations with partial derivatives, were introduced around a half century ago. Principally, the method is based on the use of bases associated with orthogonal polynomials. The discretization of an elliptic equation is then explained, which can be easily extended to more realistic problems. The treatment of complex geometries is then described, as well as the extension to instationary equations. To conclude, certain remarks are made on the implementation of all these problems.

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AUTHORS

  • Christine BERNARDI: CNRS Research Director - Jacques-Louis Lions Laboratory, UMR 7598 - Pierre and Marie Curie University, Paris 6

  • Yvon MADAY: Professor - Jacques-Louis Lions Laboratory, UMR 7598 - Pierre and Marie Curie University, Paris 6 - University Institute of France

 INTRODUCTION

Spectral methods, as a technique for discretizing partial differential equations, were introduced almost half a century ago in a series of papers by Steve Orszag (see for example ) and in . They were given a major boost by David Gottlieb and Steve Orszag's book where the foundations were laid for their generalization to approximations other than Fourier series periodic functions. They are characterized by the following two points:

  • approximation by high-degree polynomials ;

  • the use of tensorized polynomial bases.

Another aspect of this evolution is that the use of the fast Fourier transform algorithm, which was important just 15 years ago, has since been relegated to the background by increased computing power and improved matrix multiplication algorithms.

Like many other discretizations today, spectral methods make use of the variational formulation of the initial problem, and the discrete problem is most often constructed by Galerkin's method, so that the error between the exact solution and the approximated solution is of the same order as the best approximation error in discrete space. Approximation by high-degree polynomials leads to an infinite-order discretization, in the following sense: if N denotes the...

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