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Life sciences · Preprint
arXiv · September 8, 2026
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This is a preprint describing a physics-driven neural network method (LSPDNN) for solving three-dimensional electromagnetic inverse scattering problems. The authors report that their level-set-based approach produces improved boundary clarity, more uniform material regions, and reduced artifacts compared to implied baseline methods, but the abstract does not quantify these improvements or compare outcomes to established alternative approaches.
Preprint. Intervention: Level-set-based physics-driven neural network solver (LSPDNN) for 3-D electromagnetic inverse scattering, incorporating soft-union multi-material model, model-consistent total variation regularization, and adaptive loss balancing.
LSPDNN reconstructs scatterers with clear boundaries using multi-level-set neural components Model-consistent total variation regularization applied to material-region indicators reduces fragmented material assignments Adaptive loss balancing strategy reduces dependence on manually selected regularization weights
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This is a computational method development paper presenting a novel neural network algorithm for inverse scattering problems, demonstrated on numerical and experimental data but without clinical validation or comparison to established clinical standards.
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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 unknown targets with multiple coordinate-dependent neural level-set components. Specifically, a soft-union multi-material model is proposed to separately describe the object support and material distribution. The global support is formed by the union of multiple level-set components, while the local contrast is determined by normalized component weights and learnable complex permittivity candidates. In addition, a model-consistent total variation (TV) regularization is imposed on the material-region indicators, rather than directly on the reconstructed contrast, to suppress fragmented material assignments without excessively smoothing material interfaces. An adaptive loss balancing strategy is further introduced to reduce the dependence on manually selected regularization weights. For each measurement instance, the neural level-set parameters and material candidates are optimized by minimizing a physics-consistent objective function. Numerical and experimental results demonstrate that LSPDNN can reconstruct scatterers with clear boundaries, more uniform material regions, and substantially reduced background artifacts. The results highlight the advantage of the neural level-set parameterization in challenging 3-D inverse scattering cases involving irregular shapes, closely spaced objects, multiple materials, and measurement noise.
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