Inverse Scattering for Schrödinger Equation in the Frequency Domain via Data-Driven Reduced Order Modeling

In this paper we develop a numerical method for solving an inverse scattering problem of estimating the scattering potential in a Schrödinger equation from frequency domain measurements based on reduced order models (ROM). The ROM is a projection of Schrödinger operator onto a subspace spanned by its solution snapshots at certain wavenumbers. Provided the measurements are performed at these wavenumbers, the ROM can be constructed in a data-driven manner from the measurements on a surface surrounding the scatterers. Once the ROM is computed, the scattering potential can be estimated using non-linear optimization that minimizes the ROM misfit. Such an approach typically outperforms the conventional methods based on data misfit minimization. We develop two variants of ROM-based algorithms for inverse scattering and test them on a synthetic example in two spatial dimensions.

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Publication Details

Journal
SIAM Journal on Imaging Sciences
Published
2025-11-06
DOI
https://doi.org/10.1137/25m1741935
Primary Topic
Model Reduction and Neural Networks
Type
article
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article

Inverse Scattering for Schrödinger Equation in the Frequency Domain via Data-Driven Reduced Order Modeling

Tristan van Leeuwen, A. Mamonov, Andreas Tataris
SIAM Journal on Imaging Sciences
Model Reduction and Neural Networks
article

Inverse Scattering for Schrödinger Equation in the Frequency Domain via Data-Driven Reduced Order Modeling

Tristan van Leeuwen, A. Mamonov, Andreas Tataris
article en

Abstract

In this paper we develop a numerical method for solving an inverse scattering problem of estimating the scattering potential in a Schrödinger equation from frequency domain measurements based on reduced order models (ROM). The ROM is a projection of Schrödinger operator onto a subspace spanned by its solution snapshots at certain wavenumbers. Provided the measurements are performed at these wavenumbers, the ROM can be constructed in a data-driven manner from the measurements on a surface surrounding the scatterers. Once the ROM is computed, the scattering potential can be estimated using non-linear optimization that minimizes the ROM misfit. Such an approach typically outperforms the conventional methods based on data misfit minimization. We develop two variants of ROM-based algorithms for inverse scattering and test them on a synthetic example in two spatial dimensions.

SIAM Journal on Imaging SciencesVol. 18(4)
Centrum Wiskunde & Informatica (NL), University of Houston (US), Delft University of Technology (NL)
Openalex Percentile: Top 74%
Model Reduction and Neural Networks
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Inverse Scattering for Schrödinger Equation in the Frequency Domain via Data-Driven Reduced Order Modeling — Tristan van Leeuwen, A. Mamonov, et al. · SIAM Journal on Imaging Sciences (2025) | TGRS Research Map | TGRS