A Fourier continuation framework for high-order approximations
Akash Anand

TL;DR
This paper analyzes the convergence rates of a Fourier continuation method for high-order function approximation on [0,1], addressing Gibbs phenomenon issues and providing theoretical guarantees supported by numerical experiments.
Contribution
It introduces a Fourier continuation framework with a two-point Hermite interpolation strategy, enabling rigorous convergence analysis and practical implementation for equispaced grid data.
Findings
Convergence order of r+1 for functions in C^{r,1}([0,1])
Simplified implementation via Hermite interpolation
Numerical experiments confirm theoretical convergence rates
Abstract
It is well known that approximation of functions on whose periodic extension is not continuous fail to converge uniformly due to rapid Gibbs oscillations near the boundary. Among several approaches that have been proposed toward the resolution of Gibbs phenomenon in recent years, a Fourier continuation (FC) based approximation scheme has been suggested by Bruno and collaborators in the context of certain PDE solvers where approximation grids used are equispaced. While the practical efficacy of FC based schemes in obtaining a high-order numerical solution of PDEs is well known, theoretical convergence analyses largely remain unavailable. The primary objective of this paper is to take a step in this direction where we analyze the convergence rates of a Fourier continuation framework for approximations based on discrete functional data coming from equispaced grids. In this context,…
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Taxonomy
TopicsModel Reduction and Neural Networks · Probabilistic and Robust Engineering Design · Image and Signal Denoising Methods
