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A Neural Network–Enhanced Born Approximation for Inverse Scattering.

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Title: A Neural Network–Enhanced Born Approximation for Inverse Scattering.
Authors: Desai, Ansh1 (AUTHOR) adesai@udel.edu, Ma, Jonathan1 (AUTHOR) johnma@udel.edu, Lähivaara, Timo2 (AUTHOR) timo.lahivaara@uef.fi, Monk, Peter1 (AUTHOR) monk@udel.edu
Source: SIAM Journal on Imaging Sciences. 2026, Vol. 19 Issue 1, p302-326. 25p.
Subjects: Born approximation, Convolutional neural networks, Light scattering, Inverse scattering transform, Artificial neural networks, Empirical research, Refractive index, Sound wave 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. [ABSTRACT FROM AUTHOR]
Copyright of SIAM Journal on Imaging Sciences is the property of Society for Industrial & Applied Mathematics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of SIAM Journal on Imaging Sciences is the property of Society for Industrial & Applied Mathematics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1137/25M174499X
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      – Code: eng
        Text: English
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        PageCount: 25
        StartPage: 302
    Subjects:
      – SubjectFull: Born approximation
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Light scattering
        Type: general
      – SubjectFull: Inverse scattering transform
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Empirical research
        Type: general
      – SubjectFull: Refractive index
        Type: general
      – SubjectFull: Sound wave scattering
        Type: general
    Titles:
      – TitleFull: A Neural Network–Enhanced Born Approximation for Inverse Scattering.
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            NameFull: Desai, Ansh
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            NameFull: Ma, Jonathan
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            NameFull: Lähivaara, Timo
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              M: 01
              Text: 2026
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              Y: 2026
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