PARAMETRIC SHAPE HOLOMORPHY OF BOUNDARY INTEGRAL OPERATORS WITH APPLICATIONS\ast

被引:1
|
作者
Doelz, Jurgen [1 ,2 ]
Henriquez, Fernando
机构
[1] Univ Bonn, Inst Numer Simulat, Friedrich Hirzebruch Allee 7, D-53115 Bonn, Germany
[2] Ecole Polytech Fed Lausanne, Chair Computat Math & Simulat Sci, CH-1015 Lausanne, Switzerland
关键词
parametric regularity; boundary integral operators; acoustic scattering; sparse polynomial approximation; Bayesian inversion; neural network approximation; QUASI-MONTE CARLO; UNCERTAINTY QUANTIFICATION; FRECHET DIFFERENTIABILITY; APPROXIMATION; DERIVATIVES; SCATTERING; EQUATIONS; FIELD;
D O I
10.1137/23M1576451
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
We consider a family of boundary integral operators supported on a collection of parametrically defined bounded Lipschitz boundaries. Consequently, the boundary integral operators themselves also depend on the parametric variables, thus leading to a parameter-to-operator map. The main result of this article is to establish the analytic or holomorphic dependence of said boundary integral operators upon the parametric variables, i.e., of the parameter-to-operator map. As a direct consequence we also establish holomorphic dependence of solutions to boundary integral equations, i.e., holomorphy of the parameter-to-solution map. To this end, we construct a holomorphic extension to complex-valued boundary deformations and investigate the complex Fre'\chet differentiability of boundary integral operators with respect to each parametric variable. The established parametric holomorphy results have been identified as a key property to overcome the so-called curse of dimensionality in the approximation of parametric maps with distributed, high-dimensional inputs. To demonstrate the applicability of the derived results, we consider as a concrete example the sound-soft Helmholtz acoustic scattering problem and its frequency-robust boundary integral formulations. For this particular application, we explore the consequences of our results in reduced order modelling, Bayesian shape inversion, and the construction of efficient surrogates using artificial neural networks.
引用
收藏
页码:6731 / 6767
页数:37
相关论文
共 50 条