Semilinear elliptic eigenvalue problem: Parametric analyticity and the uncertainty quantification

Fuente: arXiv
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Main Author: Bahn, Byeong-Ho
Format: Preprint
Published: 2023
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author Bahn, Byeong-Ho
author_facet Bahn, Byeong-Ho
contents In this paper, to the best of our knowledge, we make the first attempt at studying the parametric semilinear elliptic eigenvalue problems with the parametric coefficient and some power-type nonlinearities. The parametric coefficient is assumed to have an affine dependence on the countably many parameters with an appropriate class of sequences of functions. In this paper, we obtain the upper bound estimation for the mixed derivatives of the ground eigenpairs that has the same form obtained recently for the linear eigenvalue problem. The three most essential ingredients for this estimation are the parametric analyticity of the ground eigenpairs, the uniform boundedness of the ground eigenpairs, and the uniform positive differences between ground eigenvalues of linear operators. All these three ingredients need new techniques and a careful investigation of the nonlinear eigenvalue problem that will be presented in this paper. As an application, considering each parameter as a uniformly distributed random variable, we estimate the expectation of the eigenpairs using a randomly shifted quasi-Monte Carlo lattice rule and show the dimension-independent error bound.
format Preprint
id arxiv_https___arxiv_org_abs_2308_03159
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Semilinear elliptic eigenvalue problem: Parametric analyticity and the uncertainty quantification
Bahn, Byeong-Ho
Numerical Analysis
65N35 (Primary) 65D30, 35A23 (Secondary)
In this paper, to the best of our knowledge, we make the first attempt at studying the parametric semilinear elliptic eigenvalue problems with the parametric coefficient and some power-type nonlinearities. The parametric coefficient is assumed to have an affine dependence on the countably many parameters with an appropriate class of sequences of functions. In this paper, we obtain the upper bound estimation for the mixed derivatives of the ground eigenpairs that has the same form obtained recently for the linear eigenvalue problem. The three most essential ingredients for this estimation are the parametric analyticity of the ground eigenpairs, the uniform boundedness of the ground eigenpairs, and the uniform positive differences between ground eigenvalues of linear operators. All these three ingredients need new techniques and a careful investigation of the nonlinear eigenvalue problem that will be presented in this paper. As an application, considering each parameter as a uniformly distributed random variable, we estimate the expectation of the eigenpairs using a randomly shifted quasi-Monte Carlo lattice rule and show the dimension-independent error bound.
title Semilinear elliptic eigenvalue problem: Parametric analyticity and the uncertainty quantification
topic Numerical Analysis
65N35 (Primary) 65D30, 35A23 (Secondary)
url https://arxiv.org/abs/2308.03159