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arXiv · 2602.24147

A neural operator framework for solving inverse scattering problems

Victor Chenu·Houssem Haddar·Hadrien Montanelli
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Abstract

We present a neural operator framework for solving inverse scattering problems. A neural operator produces a preliminary indicator function for the scatterer, which, after appropriate rescaling, is used as a regularization parameter within the Linear Sampling Method to validate the initial reconstruction. The neural operator is implemented as a DeepONet with a fixed radial-basis-function trunk, while the noise level required for rescaling is estimated using a dedicated neural network. A neural tangent kernel analysis guides the architectural design, reducing the network tuning to a single discretization parameter, adjustable according to the wavelength. Two-dimensional numerical experiments demonstrate the method's effectiveness, with a Python toolbox provided for reproducibility.

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Victor Chenu, Houssem Haddar, Hadrien Montanelli. 2026-02-27. A neural operator framework for solving inverse scattering problems. https://arxiv.org/abs/2602.24147

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