arXiv Machine Learning August 11, 2026 By Yutong Du, Zicheng Liu, Bo Qi, Yali Zong, Peixian Han

Coordinate-Residual Physics-Driven Neural Network for Electromagnetic Inverse Scattering

Read the original on arXiv Machine Learning →

arXiv:2608. 09382v1 Announce Type: cross Abstract: Electromagnetic inverse scattering is a nonlinear and ill-posed problem, where accurate reconstruction is challenging due to measurement limitations, noise, and high computational costs, especially for 3-D imaging.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 5

ScoreField: Neural Inverse Scattering with Score-Based Generative Priors

arXiv:2608. 02937v1 Announce Type: cross Abstract: Designing an effective electromagnetic inverse-scattering solver requires faithful enforcement of nonlinear full-wave physics together with an expressive prior on the unknown permittivity contrast.

By Wenhan Guo, Yuan Gao, Yu Sun

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Inductively Scalable, Single-Step Neural Surrogates for Wave-Scattering Inverse Problems

arXiv:2608. 17344v1 Announce Type: cross Abstract: Neural network surrogates are an emerging alternative to traditional electromagnetic wave simulators like finite-difference time-domain (FDTD); their goal is to replace rigorous physical simulations with pre-trained neural networks that solve wave-scattering forward and inverse problems orders of magnitude faster.

By Charles Dove, Laura Waller

arXiv Machine Learning

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A neural operator view on U-Nets for inverse imaging problems

arXiv:2608. 05839v1 Announce Type: cross Abstract: Deep neural networks have shown great empirical success in the solution of a wide variety of ill-posed inverse problems in imaging.

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arXiv Machine Learning

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Recovering Sharp Conductivity Features in the Finite-Data Calder\'on Problem with Physics-Informed Neural Networks

arXiv:2606. 28158v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have recently emerged as a promising framework for addressing the Calder\'on inverse problem from limited boundary data.

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arXiv AI

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AI-interpreted Optical Scattering for Robust and Focal Depth-Aware Imaging

arXiv:2607. 22867v1 Announce Type: cross Abstract: Optical scattering has conventionally been regarded as an impediment in imaging research due to the degradation of image quality during reconstruction.

By Eunji Ko, Patrick Ross, Corey Hart, Wolfgang Losert

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arXiv Machine Learning

Jul 16

Leveraging Differentiable PDE Solvers for Semi-Neural Spatial Reconstruction From Sparse Measurements

arXiv:2601. 20496v2 Announce Type: replace-cross Abstract: Generating dense physical fields from sparse measurements is a fundamental question in sampling, signal processing, and many other applications.

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