Analysis of a superconvergent recursive moving least squares approximation

The recursive moving least squares (MLS) approximation is a superconvergent technique for constructing shape functions in meshless methods. Computational formulas, properties and theoretical error of the recursive MLS approximation are analyzed in this paper. Theoretical results reveal that high order derivatives of the approximation have the same convergence order as the first order derivative. Numerical results confirm the superconvergence of the recursive MLS approximation. (C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper analyzes the computational formulas, properties, and theoretical error of the recursive MLS approximation, revealing that the high-order derivatives of the approximation have the same convergence order as the first-order derivative. Numerical results confirm the superconvergence of the recursive MLS approximation.