Finite Element Error Analysis for Elliptic Parameter Identification with Power-Type Nonlinearity

IF 3.2 2区 数学 Q1 MATHEMATICS, APPLIED
De-Han Chen, Yi-Hsuan Lin, Irwin Yousept
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引用次数: 0

Abstract

SIAM Journal on Numerical Analysis, Volume 64, Issue 5, Page 2151-2176, October 2026.
Abstract. This paper studies the numerical analysis of a parameter identification problem governed by elliptic equations with power-type nonlinearity. We propose a numerical reconstruction via a suitable least-squares minimization problem based on piecewise linear finite elements. As one of our main novelties, we establish conditional stability estimates at the continuous level, which form the theoretical foundation of the present finite element analysis. Our stability analysis relies on tailored analytical tools, including Hardy-type inequalities, fractional Gagliardo–Nirenberg inequalities, and weighted spaces with singular distance weights. By invoking the achieved conditional stability together with the Carstensen quasi-interpolation operator and associated estimates in negative Sobolev spaces, we derive a priori error estimates for the proposed finite element approximation in terms of the mesh size, the regularization parameter, the noise level, and the nonlinearity exponent. Our results extend the recent stability and error estimates for the linear case by Jin et al. [] and sharpen their error estimates and convergence order under weaker regularity assumptions.
幂型非线性椭圆参数辨识的有限元误差分析
SIAM数值分析杂志,64卷,第5期,2151-2176页,2026年10月。摘要。本文研究了一类幂型非线性椭圆方程参数辨识问题的数值分析。本文提出了一种基于分段线性有限元的合适最小二乘最小化问题的数值重构方法。作为我们的主要创新之一,我们在连续水平上建立了条件稳定性估计,这构成了目前有限元分析的理论基础。我们的稳定性分析依赖于定制的分析工具,包括hardy型不等式、分数阶Gagliardo-Nirenberg不等式和奇异距离权重的加权空间。通过调用实现条件稳定一起Carstensen quasi-interpolation运营商和相关估计负水列夫空间,我们得到先验误差估计提出了有限元近似的网格大小,正则化参数,噪声和非线性指数。我们的结果扩展了Jin等人[]最近对线性情况的稳定性和误差估计,并在较弱的正则性假设下锐化了他们的误差估计和收敛顺序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.80
自引率
6.90%
发文量
110
审稿时长
4-8 weeks
期刊介绍: SIAM Journal on Numerical Analysis (SINUM) contains research articles on the development and analysis of numerical methods. Topics include the rigorous study of convergence of algorithms, their accuracy, their stability, and their computational complexity. Also included are results in mathematical analysis that contribute to algorithm analysis, and computational results that demonstrate algorithm behavior and applicability.
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