New Sampling Algorithms and Inverse Spectral Methods in Scattering

散射中的新采样算法和逆谱方法

基本信息

  • 批准号:
    2107891
  • 负责人:
  • 金额:
    $ 19.08万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-07-15 至 2024-06-30
  • 项目状态:
    已结题

项目摘要

Non-destructive testing has many applications in engineering and medical imaging. Innovations in non-destructive testing have given rise to new imaging methods such as electrical impedance tomography (EIT), which can be used in medical applications as an alternative to more expensive imaging modalities. In many other instances as well, one needs to determine the interior structure of an object with little a priori information using acoustic or electromagnetic waves. This project aims to develop new algorithms for fast and accurate reconstructions that will be able to determine interior features. The investigator and graduate students will study new methods and applications for imaging techniques such as EIT. The primary result of the project will be new mathematical techniques for shape recovery that are largely independent of unknown physical properties of an object. The research has three parts that are connected to the analytical and computational aspects of inverse scattering. The first project is to develop new theoretically rigorous and computationally simple regularization algorithms for the factorization method. The project will focus on electrical impedance tomography, where the theory will be extended for an operator mapping a Hilbert space into the dual space. The goal is to develop the analytical framework for a new algorithm for shape reconstruction. The second project is to extend the applicability of the direct sampling method (DSM) to near-field data as well as reduce the number of measurements needed. These are simple algorithms for shape reconstruction stable with respect to noise in the data but requiring measurements from many sources and receivers. The aim is to develop the resolution analysis for new DSMs. The new methods involve transforming near-field data into far-field data and applying factorization of the far-field operator. The third project is to study new transmission eigenvalue problems arising in inverse scattering. These eigenvalues can be used to estimate or determine if there are defects in a scatterer. The reconstruction methods studied in the first two parts of the project correspond to shape reconstruction, whereas the study of these spectral problems will lead to parameter identification. Results of the research are expected not only to allow recovery of a scattering object but also to provide information about its material parameters.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
无损检测在工程和医学成像中有许多应用。无损检测的创新已经产生了新的成像方法,例如电阻抗断层扫描(EIT),它可以在医疗应用中作为更昂贵的成像方式的替代品。在许多其他情况下,人们也需要使用声波或电磁波来确定物体的内部结构,而先验信息很少。该项目旨在开发新的算法,用于快速准确的重建,从而能够确定内部特征。研究人员和研究生将研究成像技术的新方法和应用,如EIT。该项目的主要成果将是用于形状恢复的新数学技术,这些技术在很大程度上独立于物体的未知物理特性。该研究有三个部分,连接到逆散射的分析和计算方面。第一个项目是开发新的理论上严格和计算简单的正则化算法的因式分解方法。该项目将侧重于电阻抗断层成像,其中理论将被扩展为一个算子映射到希尔伯特空间的对偶空间。我们的目标是开发一个新的算法形状重建的分析框架。第二个项目是将直接采样方法(DSM)的适用性扩展到近场数据,并减少所需的测量次数。这些是形状重建的简单算法,相对于数据中的噪声稳定,但需要从许多源和接收器的测量。目的是发展新的DSM的分辨率分析。新方法涉及近场数据转换为远场数据和远场算子的因式分解。 第三个项目是研究逆散射中出现的新的透射本征值问题。这些本征值可用于估计或确定散射体中是否存在缺陷。本项目前两部分研究的重建方法对应于形状重建,而这些光谱问题的研究将导致参数识别。该研究的结果预计不仅允许恢复的散射对象,但也提供有关其材料参数的信息。该奖项反映了NSF的法定使命,并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Regularization of the factorization method with applications to inverse scattering
因式分解方法的正则化及其在逆散射中的应用
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Harris, Isaac
  • 通讯作者:
    Harris, Isaac
Analysis of the transmission eigenvalue problem with two conductivity parameters
  • DOI:
    10.1080/00036811.2023.2181167
  • 发表时间:
    2022-09
  • 期刊:
  • 影响因子:
    1.1
  • 作者:
    R. Ayala;I. Harris;A. Kleefeld;Nikolaos Pallikarakis
  • 通讯作者:
    R. Ayala;I. Harris;A. Kleefeld;Nikolaos Pallikarakis
Regularization of the factorization method applied to diffuse optical tomography
  • DOI:
    10.1088/1361-6420/ac37f9
  • 发表时间:
    2021-06
  • 期刊:
  • 影响因子:
    2.1
  • 作者:
    I. Harris
  • 通讯作者:
    I. Harris
Reconstruction of small and extended regions in EIT with a Robin transmission condition
  • DOI:
    10.1088/1361-6420/ac8b2e
  • 发表时间:
    2022-03
  • 期刊:
  • 影响因子:
    2.1
  • 作者:
    G. Granados;I. Harris
  • 通讯作者:
    G. Granados;I. Harris
Direct Sampling for Recovering Sound Soft Scatterers from Point Source Measurements
  • DOI:
    10.3390/computation9110120
  • 发表时间:
    2021-10
  • 期刊:
  • 影响因子:
    0
  • 作者:
    I. Harris
  • 通讯作者:
    I. Harris
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Isaac Harris其他文献

Reconstruction of extended regions in EIT with a generalized Robin transmission condition
用广义 Robin 传输条件重建 EIT 中的扩展区域
Metal-Optic Nanophotonic Modulators in Standard CMOS Technology
标准 CMOS 技术中的金属光学纳米光子调制器
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    M. Elkabbash;Sivan Trajtenberg‐Mills;Isaac Harris;S. Bandyopadhyay;Mohamed I. Ibrahim;Archer Wang;Xibi Chen;Cole J. Brabec;Hasan Z. Yildiz;Ruonan Han;Dirk Englund
  • 通讯作者:
    Dirk Englund
A wireless terahertz cryogenic interconnect that minimizes heat-to-information transfer
一种使热到信息传输最小化的无线太赫兹低温互连
  • DOI:
    10.1038/s41928-025-01355-9
  • 发表时间:
    2025-03-10
  • 期刊:
  • 影响因子:
    40.900
  • 作者:
    Jinchen Wang;Isaac Harris;Mohamed Ibrahim;Dirk Englund;Ruonan Han
  • 通讯作者:
    Ruonan Han

Isaac Harris的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Isaac Harris', 18)}}的其他基金

Direct and Inverse Scattering in Biharmonic Waves: Analysis and Computation
双谐波中的正向和逆向散射:分析和计算
  • 批准号:
    2208256
  • 财政年份:
    2022
  • 资助金额:
    $ 19.08万
  • 项目类别:
    Continuing Grant

相似海外基金

CAREER: Towards Tight Guarantees of Markov Chain Sampling Algorithms in High Dimensional Statistical Inference
职业:高维统计推断中马尔可夫链采样算法的严格保证
  • 批准号:
    2237322
  • 财政年份:
    2023
  • 资助金额:
    $ 19.08万
  • 项目类别:
    Continuing Grant
Collaborative Research: Dynamical Sampling on Graphs: Mathematical Framework and Algorithms
协作研究:图动态采样:数学框架和算法
  • 批准号:
    2208031
  • 财政年份:
    2022
  • 资助金额:
    $ 19.08万
  • 项目类别:
    Standard Grant
Collaborative Research: Dynamical Sampling on Graphs: Mathematical Framework and Algorithms
协作研究:图动态采样:数学框架和算法
  • 批准号:
    2208030
  • 财政年份:
    2022
  • 资助金额:
    $ 19.08万
  • 项目类别:
    Standard Grant
Biased sampling algorithms for scientific and engineering applications
用于科学和工程应用的偏置采样算法
  • 批准号:
    RGPIN-2020-03907
  • 财政年份:
    2022
  • 资助金额:
    $ 19.08万
  • 项目类别:
    Discovery Grants Program - Individual
CRII: AF: Optimization and sampling algorithms with provable generalization and runtime guarantees, with applications to deep learning
CRII:AF:具有可证明的泛化性和运行时保证的优化和采样算法,以及深度学习的应用
  • 批准号:
    2104528
  • 财政年份:
    2021
  • 资助金额:
    $ 19.08万
  • 项目类别:
    Standard Grant
Tensor decomposition sampling algorithms for Bayesian inverse problems
贝叶斯逆问题的张量分解采样算法
  • 批准号:
    EP/T031255/1
  • 财政年份:
    2021
  • 资助金额:
    $ 19.08万
  • 项目类别:
    Research Grant
Biased sampling algorithms for scientific and engineering applications
用于科学和工程应用的偏置采样算法
  • 批准号:
    RGPIN-2020-03907
  • 财政年份:
    2021
  • 资助金额:
    $ 19.08万
  • 项目类别:
    Discovery Grants Program - Individual
Mechanics-Based Algorithms for Sampling, Control, and Learning in Non-Convex Domains
基于力学的非凸域采样、控制和学习算法
  • 批准号:
    2122856
  • 财政年份:
    2021
  • 资助金额:
    $ 19.08万
  • 项目类别:
    Standard Grant
Improved algorithms via random sampling
通过随机采样改进算法
  • 批准号:
    DP210103849
  • 财政年份:
    2021
  • 资助金额:
    $ 19.08万
  • 项目类别:
    Discovery Projects
Biased sampling algorithms for scientific and engineering applications
用于科学和工程应用的偏置采样算法
  • 批准号:
    RGPIN-2020-03907
  • 财政年份:
    2020
  • 资助金额:
    $ 19.08万
  • 项目类别:
    Discovery Grants Program - Individual
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了