Least squares solvers for ill-posed PDEs that are conditionally stable

Least squares solvers for ill-posed PDEs that are conditionally stable
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用于条件稳定的不适定偏微分方程的最小二乘求解器

DOI:
10.1051/m2an/2023050
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发表时间:
2023
期刊:
ESAIM: Mathematical Modelling and Numerical Analysis
影响因子:
--
通讯作者:
Stevenson, Rob
Stevenson, Rob
中科院分区:
--
文献类型:
--
作者:
Dahmen, Wolfgang;Monsuur, Harald;Stevenson, Rob

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本文研究条件稳定的不适定偏微分方程的最小二乘解的设计和分析。最小二乘泛函中使用的范数和正则化项由条件稳定性假设的成分确定。然后,我们能够建立一个一般的误差界,鉴于条件稳定性假设,是定性的最好的可能,而不假设一致的数据。这些优点的代价是处理对偶范数,这减少了验证适当的inf-sup稳定性。反过来,这是通过为所有示例场景构建适当的Fortin投影仪来完成的。数值实验结果说明了理论研究结果。
This paper is concerned with the design and analysis of least squares solvers for ill-posed PDEs that are conditionally stable. The norms and the regularization term used in the least squares functional are determined by the ingredients of the conditional stability assumption. We are then able to establish a general error bound that, in view of the conditional stability assumption, is qualitatively the best possible, without assuming consistent data. The price for these advantages is to handle dual norms which reduces to verifying suitable inf-sup stability. This, in turn, is done by constructing appropriate Fortin projectors for all sample scenarios. The theoretical findings are illustrated by numerical experiments.
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