A direct sampling-based deep learning approach for inverse medium scattering problems
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Publication:6141551
DOI10.1088/1361-6420/ad0dbaarXiv2305.00250OpenAlexW4388750071MaRDI QIDQ6141551
Fuqun Han, Unnamed Author, Jun Zou
Publication date: 20 December 2023
Published in: Inverse Problems (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2305.00250
Artificial neural networks and deep learning (68T07) Inverse problems for PDEs (35R30) Laplace operator, Helmholtz equation (reduced wave equation), Poisson equation (35J05) Numerical solution to inverse problems in abstract spaces (65J22)
Cites Work
- Unnamed Item
- A qualitative approach to inverse scattering theory
- A direct sampling method for inverse scattering using far-field data
- Inverse acoustic and electromagnetic scattering theory.
- A direct sampling method for simultaneously recovering electromagnetic inhomogeneous inclusions of different nature
- On an artificial neural network for inverse scattering problems
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- The singular sources method for an inverse transmission problem
- Reverse Time Migration for Extended Obstacles: Acoustic Waves
- A direct sampling method for electrical impedance tomography
- Investigation of Preconditioning Techniques for the Iteratively Regularized Gauss–Newton Method for Exponentially Ill-Posed Problems
- The Linear Sampling Method in Inverse Electromagnetic Scattering
- The MUSIC-algorithm and the factorization method in inverse scattering theory for inhomogeneous media
- A direct sampling method to an inverse medium scattering problem
- Inverse scattering problems with multi-frequencies
- Inverse scattering via Heisenberg's uncertainty principle
- A contrast source inversion method
- Solving ill-posed inverse problems using iterative deep neural networks
- Direct sampling methods for inverse elastic scattering problems
- Marine Acoustics
- Construct Deep Neural Networks based on Direct Sampling Methods for Solving Electrical Impedance Tomography
- Least-squares method for recovering multiple medium parameters
- Orthogonality Sampling Method for the Electromagnetic Inverse Scattering Problem
- NETT: solving inverse problems with deep neural networks
- Solving inverse problems using data-driven models
- SwitchNet: A Neural Network Model for Forward and Inverse Scattering Problems
- Direct Sampling Method for Diffusive Optical Tomography
- High Resolution Inverse Scattering in Two Dimensions Using Recursive Linearization
- A survey on sampling and probe methods for inverse problems
- Locating a complex inhomogeneous medium with an approximate factorization method
- Inverse medium scattering for the Helmholtz equation at fixed frequency
- A direct sampling method for inverse electromagnetic medium scattering
- A Direct Sampling Method for the Inversion of the Radon Transform
- The linear sampling method and the MUSIC algorithm
- An introduction to the mathematical theory of inverse problems
- Deep learning methods for partial differential equations and related parameter identification problems
- A neural network warm-start approach for the inverse acoustic obstacle scattering problem
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