Generalized regularized least-squares approximation of noisy data with application to stochastic PDEs

被引：6

作者：

Shirzadi, Mohammad ^{[1
]}

Dehghan, Mehdi ^{[1
]}

机构：

[1] Amirkabir Univ Technol, Fac Math & Comp Sci, Dept Appl Math, 424 Hafez Ave, Tehran, Iran

来源：

APPLIED MATHEMATICS LETTERS | 2021年 / 111卷 / 111期

关键词：

Radial basis function; Regularized least-squares; approximation; Noisy data; Stochastic PDEs; PARTIAL-DIFFERENTIAL-EQUATIONS; PROBABILITY DENSITY-FUNCTION; JUMP-DIFFUSION MODELS; MESHLESS METHOD; NUMERICAL-SOLUTION; INTERPOLATION;

D O I：

10.1016/j.aml.2020.106598

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The regularized least-squares radial basis approximation is a kernel-based method to approximate a set of scattered data by a least-squares fit based on an optimization procedure that balances a tradeoff between smoothness of approximation and closeness to the data via a smoothing parameter. This paper suggests the generalized regularized least-squares radial basis approximation for noisy data and its application to the numerical solution of stochastic elliptic PDEs. Numerical observations show that the proposed method is more stable than the typical kernel-based method. (C) 2020 Elsevier Ltd. All rights reserved.

引用

页数：8

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