arXiv Machine Learning August 5, 2026 By Wenhan Guo, Yuan Gao, Yu Sun

ScoreField: Neural Inverse Scattering with Score-Based Generative Priors

Read the original on arXiv Machine Learning →

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.

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

benchmarks

arXiv Machine Learning

Aug 11

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

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.

By Yutong Du, Zicheng Liu, Bo Qi, Yali Zong, Peixian Han

arXiv Machine Learning

Jun 29

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.

By Ali AlHadi Kalout, Pablo Tejerina-P\'erez, Konstantin Karchev, Pedro Taranc\'on-\'Alvarez, Leonid Sarieddine, Raul Jimenez, Max Engelstein, Guy David

arXiv AI

Jul 28

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

diffusion benchmarks

arXiv Machine Learning

Jun 2

Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture

arXiv:2606. 01110v1 Announce Type: cross Abstract: Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding.

By Hoang Anh Nguyen, Divakar Vashisth, Ali Tura

benchmarks

arXiv Machine Learning

Jun 3

Let There Be Light: Reflection, Refraction and Scattering for Neural Operators

arXiv:2606. 03262v1 Announce Type: new Abstract: Neural operators learn mappings between infinite-dimensional function spaces and provide a data-driven surrogate modeling paradigm for parametric partial differential equations (PDEs).

By Keke Wu, Yixuan Zhang, Jingrun Chen

diffusion safety

arXiv Machine Learning

Aug 4

Self-composing neural operators for high-frequency and multiscale PDE surrogates

arXiv:2508. 20650v2 Announce Type: replace Abstract: Addressing the computational challenges of high-frequency and multiscale partial differential equations (PDEs), this work introduces a self-composing neural operator (SC-NO) framework.

By Juncai He, Xinliang Liu, Jinchao Xu

safety

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