Neural Surrogate Modeling of Ultrasonic Wave Propagation in Heterogeneous Media
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Computer Science > Machine Learning
Title:Neural Surrogate Modeling of Ultrasonic Wave Propagation in Heterogeneous Media
Abstract:Neural surrogates provide computationally efficient auto-regressive modeling of wave propagation, enabling e.g. fast inverse solutions. Despite recent progress, the current surrogates are not yet sufficient for the needs of ultrasonic sensing, characterized by high frequency and heterogeneous propagation media. We show how methods building on Fourier Neural Operators (FNO) fail to capture essential signal features, such as reflections, whereas U-Net -based methods learn the wave fields well but lack the ability to transfer information across different spatial scales. We then propose a new surrogate that captures the best of the two worlds, by integrating FNOs and local-integral resampling into a U-Net architecture. We show it can be trained on limited data, and that the predictions are robust to input noise and work across spatial scales.
| Comments: | Submitted to IEEE ICASSP 2027 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.13258 [cs.LG] |
| (or arXiv:2610.13258v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.13258
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Burla Nur Korkmaz [view email][v1] Fri, 9 Oct 2026 09:07:37 UTC (2,766 KB)
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