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Sensitivity analysis of normal mode algorithms using automatic differentiation.

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Title: Sensitivity analysis of normal mode algorithms using automatic differentiation.
Authors: Vardi, Ariel1,2 (AUTHOR) arielv@mit.edu, Averbuch, Gil1 (AUTHOR), Leonard, John J.2 (AUTHOR)
Source: Journal of the Acoustical Society of America. May2026, Vol. 159 Issue 5, p4576-4589. 14p.
Subjects: Sensitivity analysis, Automatic differentiation, Acoustic wave propagation, Modal analysis, Underwater acoustics, Eigenvalues
Abstract: Acoustic propagation underwater is affected by dynamic and spatial variations of the sound speed, both of which can be observed in underwater environments. In this work, a differentiable implementation of the KRAKEN normal mode model is presented. By applying automatic differentiation to the discretized algebraic eigenvalue problem, we obtain sensitivities of every modal quantity with respect to a full set of geoacoustic parameters and frequency. These sensitivities are accurate to machine precision. This opens gradient-based methods for inversion purposes, such as source localization and geoacoustic inversion. The utility of this approach is demonstrated by performing sensitivity analyses on classical benchmark problems. These analyses provide insight into parameter significance, validate our implementation, and offer a foundation for developing more efficient gradient-based inversion techniques. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics 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.)
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DbLabel: Engineering Source
An: 194178301
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PubTypeId: academicJournal
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  Data: Sensitivity analysis of normal mode algorithms using automatic differentiation.
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  Data: <searchLink fieldCode="AR" term="%22Vardi%2C+Ariel%22">Vardi, Ariel</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> arielv@mit.edu</i><br /><searchLink fieldCode="AR" term="%22Averbuch%2C+Gil%22">Averbuch, Gil</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Leonard%2C+John+J%2E%22">Leonard, John J.</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Acoustical+Society+of+America%22">Journal of the Acoustical Society of America</searchLink>. May2026, Vol. 159 Issue 5, p4576-4589. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+differentiation%22">Automatic differentiation</searchLink><br /><searchLink fieldCode="DE" term="%22Acoustic+wave+propagation%22">Acoustic wave propagation</searchLink><br /><searchLink fieldCode="DE" term="%22Modal+analysis%22">Modal analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Underwater+acoustics%22">Underwater acoustics</searchLink><br /><searchLink fieldCode="DE" term="%22Eigenvalues%22">Eigenvalues</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Acoustic propagation underwater is affected by dynamic and spatial variations of the sound speed, both of which can be observed in underwater environments. In this work, a differentiable implementation of the KRAKEN normal mode model is presented. By applying automatic differentiation to the discretized algebraic eigenvalue problem, we obtain sensitivities of every modal quantity with respect to a full set of geoacoustic parameters and frequency. These sensitivities are accurate to machine precision. This opens gradient-based methods for inversion purposes, such as source localization and geoacoustic inversion. The utility of this approach is demonstrated by performing sensitivity analyses on classical benchmark problems. These analyses provide insight into parameter significance, validate our implementation, and offer a foundation for developing more efficient gradient-based inversion techniques. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics 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: 10.1121/10.0043870
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 14
        StartPage: 4576
    Subjects:
      – SubjectFull: Sensitivity analysis
        Type: general
      – SubjectFull: Automatic differentiation
        Type: general
      – SubjectFull: Acoustic wave propagation
        Type: general
      – SubjectFull: Modal analysis
        Type: general
      – SubjectFull: Underwater acoustics
        Type: general
      – SubjectFull: Eigenvalues
        Type: general
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      – TitleFull: Sensitivity analysis of normal mode algorithms using automatic differentiation.
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            NameFull: Vardi, Ariel
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              M: 05
              Text: May2026
              Type: published
              Y: 2026
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