Integrating Moving Least Squares with non-linear WENO method: A novel Partition of Unity approach in 1D

TL;DR

This paper introduces a new method combining Moving Least Squares with non-linear WENO to improve data approximation accuracy near discontinuities while preserving smooth region accuracy in 1D.

Contribution

It presents a novel partition of unity approach integrating MLS and WENO methods, addressing discontinuities effectively in data approximation.

Findings

Enhanced approximation accuracy near discontinuities.

Maintains order of accuracy in smooth regions.

Validated through numerical experiments.

Abstract

The approximation of data is a fundamental challenge encountered in various fields, including computer-aided geometric design, the numerical solution of partial differential equations, or the design of curves and surfaces. Numerous methods have been developed to address this issue, providing good results when the data is continuous. Among these, the Moving Least Squares (MLS) method has proven to be an effective strategy for fitting data, finding applications in both statistics and applied mathematics. However, the presence of isolated discontinuities in the data can lead to undesirable artifacts, such as the Gibbs phenomenon, which adversely affects the quality of the approximation. In this paper, we propose a novel approach that integrates the Moving Least Squares method with the well-established non-linear Weighted Essentially Non-Oscillatory (WENO) method. This combination aims to construct a non-linear operator that enhances the accuracy of approximations near discontinuities while maintaining the order of accuracy in smooth regions. We investigate the properties of this operator in one dimension, demonstrating its effectiveness through a series of numerical experiments that validate our theoretical findings.