arXiv AI August 19, 2026 By Charles Dove, Laura Waller

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.

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

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

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

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 18

Starter-Iterator Neural Operator: A Unified Architecture for High-Fidelity Forward and Inverse PDE Problems

arXiv:2606. 18305v1 Announce Type: cross Abstract: Operator learning is an emerging interdisciplinary field that integrates machine learning with scientific computing.

By Kuilin Qin, Lianfang Wang, Xu Sun, Jiwei Jia, Yu Wang, Yong Wang, Yuping Duan

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 AI

Jul 28

When Every Simulation Counts: Value-Based Reinforcement Learning for Accelerated Photonics Inverse Design

arXiv:2607. 23469v1 Announce Type: cross Abstract: Photonic-crystal surface-emitting lasers (PCSELs) can combine high-power operation with narrow-divergence surface emission, but optimizing coupled parameters requires costly full-wave simulations.

By Longying Wen, Feiyang Wu, Jinglin Yu, Chongxian Yuan, Renjie Li, Zhaoyu Zhang

reinforcement-learning

Related stories

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

ScoreField: Neural Inverse Scattering with Score-Based Generative Priors

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

Starter-Iterator Neural Operator: A Unified Architecture for High-Fidelity Forward and Inverse PDE Problems

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

When Every Simulation Counts: Value-Based Reinforcement Learning for Accelerated Photonics Inverse Design