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On the recovery of piecewise smooth functions from their spectral data and integral transforms.

机译:从其频谱数据和积分变换中恢复分段平滑函数。

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摘要

The Gibbs phenomenon refers to the lack of uniform convergence which occurs in many orthogonal basis approximations and integral transform approximations to piecewise smooth functions. This lack of uniform convergence manifests itself in spurious oscillations near the points of discontinuity and a low order of convergence away from the discontinuities.; In this dissertation we describe a new theory developed by this author for completely overcoming the Gibbs phenomenon. This new theory allows for the global recovery of a piecewise smooth function without the need for any edge detection. It also applies to approximations in any expansion basis or integral transform which suffers from the Gibbs phenomenon.; Also presented in the dissertation are generalizations and contributions to the Gegenbauer reconstruction method of Gottlieb and Shu. These include a new explicit formula for Gegenbauer reconstruction in the general case, a demonstration that Gegenbauer reconstruction can resolve the Gibbs phenomenon in a Fourier-Bessel partial sum, as well as a discussion on the problem of reconstruction for classical orthogonal polynomials apart from the issue of the Gibbs phenomenon, and a Fourier approach to formulating and solving finite difference schemes.
机译:吉布斯现象是指缺乏均匀收敛,这种收敛出现在许多正交基逼近和分段平滑函数的积分变换逼近中。这种不均匀收敛的缺乏体现在不连续点附近的伪振荡和远离不连续点的低阶收敛。在本文中,我们描述了作者完全克服吉布斯现象的一种新理论。这一新理论允许分段平滑函数的全局恢复,而无需任何边缘检测。它也适用于遭受吉布斯现象的任何扩展基或积分变换的近似。论文还对Gottlieb和Shu的Gegenbauer重建方法进行了概括和贡献。其中包括一般情况下Gegenbauer重构的新显式,Gegenbauer重构可以解决傅立叶-贝塞尔局部和的Gibbs现象,以及关于经典正交多项式的重构问题的讨论。吉布斯(Gibbs)现象的研究,以及用傅立叶方法来制定和求解有限差分方案。

著录项

  • 作者

    Greene, Nataniel.;

  • 作者单位

    State University of New York at Stony Brook.;

  • 授予单位 State University of New York at Stony Brook.;
  • 学科 Mathematics.; Computer Science.; Engineering General.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 141 p.
  • 总页数 141
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 数学;自动化技术、计算机技术;工程基础科学;
  • 关键词

  • 入库时间 2022-08-17 11:44:23

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