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Multiscale Fourier Neural Operator for Inverse Wave Scattering in Highly Oscillatory Media

arXiv CS Tuesday 09 June 2026, 04:00 UTC By Zilin You, Zhenli Xu, Wei Cai 1 min read
Key Points

Announce Type: new Abstract: In this paper, we propose an operator learning method based on the multiscale Fourier neural operator (MscaleFNO) for inverse medium problems of Helmholtz equations. The MscaleFNO provides a neural surrogate model with reduced spectral bias for the Helmholtz equations, mapping highly oscillatory medium profiles to scattered wavefields. A plug-and-play inversion using elucidated diffusion model is introduced to regularize the inverse solver based on least squares...

arXiv:2606.08448v1 Announce Type: new Abstract: In this paper, we propose an operator learning method based on the multiscale Fourier neural operator (MscaleFNO) for inverse medium problems of Helmholtz equations. The MscaleFNO provides a neural surrogate model with reduced spectral bias for the Helmholtz equations, mapping highly oscillatory medium profiles to scattered wavefields. A plug-and-play inversion using elucidated diffusion model is introduced to regularize the inverse solver based on least squares of data misfits. Numerical results for partial aperture inversion of oscillatory two-dimensional media demonstrate the advantage and effectiveness of MscaleFNO for accurate reconstruction of highly oscillatory medium properties.
Multiscale Fourier Neural Operator (ORG) Helmholtz (ORG)
Originally published by arXiv CS Read original →

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