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相关论文: On vanishing near corners of conductive transmissi…

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This paper is concerned with the intrinsic geometric structures of conductive transmission eigenfunctions. The geometric properties of interior transmission eigenfunctions were first studied in [9]. It is shown in two scenarios that the…

偏微分方程分析 · 数学 2020-06-18 Huaian Diao , Xinlin Cao , Hongyu Liu

The purpose of the paper is twofold. First, we show that partial-data transmission eigenfunctions associated with a conductive boundary condition vanish locally around a polyhedral or conic corner in $\mathbb{R}^n$, $n=2,3$. Second, we…

偏微分方程分析 · 数学 2025-04-23 Huaian Diao , Xiaoxu Fei , Hongyu Liu

We investigate the localization and vanishing of $L^2$ interior transmission eigenfunctions at corners. Past numerical computations suggest that these eigenfunctions localize at non-convex corners. This phenomenon has, however, not been…

偏微分方程分析 · 数学 2025-12-03 Emilia L. K. Blåsten , Valter Pohjola

In this addendum, we relax a restrictive assumption in [1] needed for the interior transmission eigenfunctions to hold the intrinsic geometric vanishing property in a corner. In addition we present in more detail another assumption which…

偏微分方程分析 · 数学 2017-10-24 Eemeli Blåsten , Hongyu Liu

Let $\Omega$ be a bounded domain in $\mathbb{R}^n$, $n\geq 2$, and $V\in L^\infty(\Omega)$ be a potential function. Consider the following transmission eigenvalue problem for nontrivial $v, w\in L^2(\Omega)$ and $k\in\mathbb{R}_+$,…

偏微分方程分析 · 数学 2017-10-25 Eemeli Blåsten , Hongyu Liu

This paper is concerned with the intrinsic geometric structure of interior transmission eigenfunctions arising in wave scattering theory. We numerically show that the aforementioned geometric structure can be much delicate and intriguing.…

数值分析 · 数学 2017-10-04 Eemeli Blåsten , Xiaofei Li , Hongyu Liu , Yuliang Wang

The transmission eigenvalue problem is a type of non-elliptic and non-selfadjoint spectral problem that arises in the wave scattering theory when invisibility/transparency occurs. The transmission eigenfunctions are the interior resonant…

偏微分方程分析 · 数学 2023-04-24 Yat Tin Chow , Youjun Deng , Hongyu Liu , Mahesh Sunkula

We consider the inverse source problem of a fixed wavenumber: study properties of an acoustic source based on a single far- or near-field measurement. We show that nonradiating sources having a convex or non-convex corner or edge on their…

偏微分方程分析 · 数学 2018-04-18 Eemeli Blåsten

The (interior) transmission eigenvalue problems are a type of non-elliptic, non-selfadjoint and nonlinear spectral problems that arise in the theory of wave scattering. They connect to the direct and inverse scattering problems in many…

偏微分方程分析 · 数学 2020-12-07 Hongyu Liu

This paper investigates a distinctive spectral pattern exhibited by transmission eigenfunctions in wave scattering theory. Building upon the discovery in [7, 8] that these eigenfunctions localize near the domain boundary, we derive sharp…

偏微分方程分析 · 数学 2026-03-24 Yan Jiang , Hongyu Liu , Kai Zhang , Haoran Zheng

We present the discovery of a novel and intriguing global geometric structure of the (interior) transmission eigenfunctions associated with the Helmholtz system. It is shown in generic scenarios that there always exists a sequence of…

偏微分方程分析 · 数学 2020-12-16 Yat Tin Chow , Youjun Deng , Youzi He , Hongyu Liu , Xianchao Wang

We consider the propagation of waves in a waveguide with Neumann boundary conditions. We work at low wavenumber with only one propagating mode in the leads, all the other modes being evanescent. We assume that the waveguide is symmetric…

偏微分方程分析 · 数学 2018-03-19 Lucas Chesnel , Vincent Pagneux

The impact of surface reflection on the statistics of transmission eigenvalues is a largely unexplored subject of fundamental and practical importance in statistical optics. Here, we develop a first-principles theory and confirm numerically…

无序系统与神经网络 · 物理学 2014-05-20 Xiaojun Cheng , Chushun Tian , Azriel Z. Genack

In this paper, we present a Spectral-Galerkin Method to approximate the zero-index transmission eigenvalues with a conductive boundary condition. This is a new eigenvalue problem derived from the scalar inverse scattering problem for an…

数值分析 · 数学 2020-02-27 Isaac Harris

Transmission eigenchannels and associated eigenvalues, that give a full account of wave propagation in random media, have recently emerged as a major theme in theoretical and applied optics. Here we demonstrate, both analytically and…

光学 · 物理学 2015-09-30 Liyi Zhao , Chushun Tian , Yury P. Bliokh , Valentin Freilikher

Consider the transmission eigenvalue problem for $u \in H^1(\Omega)$ and $v\in H^1(\Omega)$ associated with $(\Omega; \sigma, \mathbf{n}^2)$, where $\Omega$ is a ball in $\mathbb{R}^N$, $N=2,3$. If $\sigma$ and $\mathbf{n}$ are both…

偏微分方程分析 · 数学 2022-02-08 Yan Jiang , Hongyu Liu , Jiachuan Zhang , Kai Zhang

In this paper we show that the eigenfunctions can be found exactly for systems whose delay-Doppler spread function is concentrated along a straight line and they can be found in approximate sense for systems having a spread function…

信息论 · 计算机科学 2015-10-15 Sergio Barbarossa , Mikhail Tsitsvero

In this paper, we provide an analytical study of the transmission eigenvalue problem with two conductivity parameters. We will assume that the underlying physical model is given by the scattering of a plane wave for an isotropic scatterer.…

偏微分方程分析 · 数学 2022-09-16 Rafael Ceja Ayala , Isaac Harris , Andreas Kleefeld , Nikolaos Pallikarakis

Transmission eigenfunctions are certain interior resonant modes that are of central importance to the wave scattering theory. In this paper, we present the discovery of novel global rigidity properties of the transmission eigenfunctions…

光学 · 物理学 2021-04-15 Youjun Deng , Hongyu Liu , Xianchao Wang , Wei Wu

Transmission eigenchannels are building blocks of coherent wave transport in diffusive media, and selective excitation of individual eigenchannels can lead to diverse transport behavior. An essential yet poorly understood property is the…

光学 · 物理学 2019-08-06 Hasan Yılmaz , Chia Wei Hsu , Alexey Yamilov , Hui Cao
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