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This paper proposes a level-set-based physics-driven neural network solver (LSPDNN) for 3-D electromagnetic inverse scattering. To mitigate boundary blurring and reconstruction artifacts in voxel-wise contrast reconstruction, the proposed solver exploits the piecewise homogeneity of practical scatterers by representing...
A coordinate-residual physics-driven neural network (CRPDNN) is proposed for 3-D electromagnetic inverse scattering, whose parameters are optimized by enforcing consistency between the measured and model-predicted scattered fields and does not require a preliminary reconstruction, thereby avoiding dependence on its acc...
Electromagnetic inverse scattering is a nonlinear and ill-posed problem, where accurate reconstruction is challenging due to measurement limitations, noise, and high computational costs, especially for 3-D imaging. Although physics-driven neural networks (PDNNs) reduce the dependence on labeled training data, existing...
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