On a Hybrid Approach for Recovering Multiple Obstacles
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Publication:5065195
DOI10.4208/cicp.OA-2021-0124zbMath1482.62044MaRDI QIDQ5065195
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Publication date: 18 March 2022
Published in: Communications in Computational Physics (Search for Journal in Brave)
Bayesian inference (62F15) Inverse problems for PDEs (35R30) Numerical methods for inverse problems for boundary value problems involving PDEs (65N21) Inverse problems (including inverse scattering) in optics and electromagnetic theory (78A46)
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Cites Work
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- A Bayesian level set method for geometric inverse problems
- Stable determination of sound-hard polyhedral scatterers by a minimal number of scattering measurements
- A neural network scheme for recovering scattering obstacles with limited phaseless far-field data
- Bayesian method for shape reconstruction in the inverse interior scattering problem
- On-surface radiation condition for multiple scattering of waves
- Singular perturbation of reduced wave equation and scattering from an embedded obstacle
- On an artificial neural network for inverse scattering problems
- The interior inverse scattering problem for a two-layered cavity using the Bayesian method
- On nodal and generalized singular structures of Laplacian eigenfunctions and applications to inverse scattering problems
- Recovering a polyhedral obstacle by a few backscattering measurements
- Hybrid Newton-type methods for reconstructing sound-soft obstacles from a single far field
- Two Single-Shot Methods for Locating Multiple Electromagnetic Scatterers
- Inverse problems: A Bayesian perspective
- An Analysis of Infinite Dimensional Bayesian Inverse Shape Acoustic Scattering and Its Numerical Approximation
- Strengthened Linear Sampling Method with a Reference Ball
- On unique determination of partially coated polyhedral scatterers with far field measurements
- A global uniqueness for formally determined inverse electromagnetic obstacle scattering
- A study on orthogonality sampling
- Multilevel Linear Sampling Method for Inverse Scattering Problems
- On anomalous localized resonance and plasmonic cloaking beyond the quasi-static limit
- A simple method for solving inverse scattering problems in the resonance region
- A deterministic-statistical approach to reconstruct moving sources using sparse partial data
- Scattering by Curvatures, Radiationless Sources, Transmission Eigenfunctions, and Inverse Scattering Problems
- A high-frequency boundary element method for scattering by a class of multiple obstacles
- Extended-Sampling-Bayesian Method for Limited Aperture Inverse Scattering Problems
- Direct Imaging of Inhomogeneous Obstacles in a Three-Layered Ocean Waveguide
- On a novel inverse scattering scheme using resonant modes with enhanced imaging resolution
- Inverse Acoustic and Electromagnetic Scattering Theory
- Locating Multiple Multiscale Acoustic Scatterers
- Uniqueness in an inverse acoustic obstacle scattering problem for both sound-hard and sound-soft polyhedral scatterers
- Zeros of the Bessel and spherical Bessel functions and their applications for uniqueness in inverse acoustic obstacle scattering
- Unique continuation from a generalized impedance edge-corner for Maxwell’s system and applications to inverse problems
- On Novel Geometric Structures of Laplacian Eigenfunctions in $\mathbb{R}^3$ and Applications to Inverse Problems
- Surface-Localized Transmission Eigenstates, Super-resolution Imaging, and Pseudo Surface Plasmon Modes
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