Hilbert–Schmidt regularity of symmetric integral operators on bounded domains with applications to SPDE approximations

IF 0.8 4区 数学 Q3 MATHEMATICS, APPLIED Stochastic Analysis and Applications Pub Date : 2021-07-21 DOI:10.1080/07362994.2022.2053541
M. Kov'acs, A. Lang, A. Petersson
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引用次数: 5

Abstract

Abstract Regularity estimates for an integral operator with a symmetric continuous kernel on a convex bounded domain are derived. The covariance of a mean-square continuous random field on the domain is an example of such an operator. The estimates are of the form of Hilbert–Schmidt norms of the integral operator and its square root, composed with fractional powers of an elliptic operator equipped with homogeneous boundary conditions of either Dirichlet or Neumann type. These types of estimates, which couple the regularity of the driving noise with the properties of the differential operator, have important implications for stochastic partial differential equations on bounded domains as well as their numerical approximations. The main tools used to derive the estimates are properties of reproducing kernel Hilbert spaces of functions on bounded domains along with Hilbert–Schmidt embeddings of Sobolev spaces. Both non-homogeneous and homogeneous kernels are considered. In the latter case, results in a general Schatten class norm are also provided. Important examples of homogeneous kernels covered by the results of the paper include the class of Matérn kernels.
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有界域上对称积分算子的Hilbert-Schmidt正则性及其在SPDE逼近中的应用
导出了凸有界域上具有对称连续核的积分算子的抽象正则性估计。域上的均方连续随机场的协方差就是这样一个算子的例子。估计是积分算子及其平方根的Hilbert–Schmidt范数的形式,由配备Dirichlet或Neumann型齐次边界条件的椭圆算子的分数幂组成。这些类型的估计将驱动噪声的规律性与微分算子的性质相结合,对有界域上的随机偏微分方程及其数值近似具有重要意义。用于推导估计的主要工具是有界域上函数的重生成核Hilbert空间的性质,以及Sobolev空间的Hilbert–Schmidt嵌入。同时考虑非齐次核和齐次核。在后一种情况下,还提供了一般Schatten类范数的结果。本文结果所涵盖的齐次核的重要例子包括Matérn核类。
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
Stochastic Analysis and Applications
Stochastic Analysis and Applications 数学-统计学与概率论
CiteScore
2.70
自引率
7.70%
发文量
32
审稿时长
6-12 weeks
期刊介绍: Stochastic Analysis and Applications presents the latest innovations in the field of stochastic theory and its practical applications, as well as the full range of related approaches to analyzing systems under random excitation. In addition, it is the only publication that offers the broad, detailed coverage necessary for the interfield and intrafield fertilization of new concepts and ideas, providing the scientific community with a unique and highly useful service.
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