A Neural Network–Enhanced Born Approximation for Inverse Scattering

Abstract. Time-harmonic acoustic inverse scattering concerns the ill-posed and nonlinear problem of determining the refractive index of an inaccessible, penetrable scatterer based on far-field wave scattering data. When the scattering is weak, the regularized inverse Born approximation provides a linearized model for recovering the shape and material properties of a scatterer. We propose two convolutional neural network (CNN) algorithms to correct the traditional inverse Born approximation even when the scattering is not weak. These are denoted Born-CNN (BCNN) and CNN-Born (CNNB). BCNN applies a postcorrection to the Born reconstruction, while CNNB precorrects the data. Both methods leverage the Born approximation’s excellent fidelity in weak scattering while extending its applicability beyond its theoretical limits. CNNB particularly exhibits a strong generalization to more complex out-of-distribution scatterers. Based on numerical tests and benchmarking against other standard approaches, our corrected Born models provide alternative data-driven methods for obtaining the refractive index, extending the utility of the Born approximation to regimes where the traditional method fails.