The paper introduces a simplified Physics-Informed Neural Network (PINN) that uses trainable Gabor basis functions to better capture the localized, oscillatory nature of acoustic wavefields, addressing the low‑frequency bias of traditional PINNs. By redefining the network’s task as learning a nonlinear mapping to a custom Gabor coordinate system, the method absorbs Gabor parameters into the mapping, reducing computational complexity and eliminating manual hyperparameter tuning. Numerical experiments on various velocity models demonstrate faster convergence, higher accuracy, and greater robustness compared to conventional PINNs and earlier Gabor‑based approaches, with the implementation made publicly available for reproducibility.
By Mohammad Mahdi Abedi, David Pardo, Tariq Alkhalifah
HarmoCore introduces a generative prior in a compact, continuous latent space for reconstructing oscillatory wave fields from extremely sparse sensor data. It models joint real–imaginary channels using Functional Tucker cores over shared spatial bases, learns a frequency‑conditioned diffusion prior, and performs diffusion posterior sampling directly in core space. Experiments on 2D and 3D Helmholtz problems demonstrate significant performance gains with only 1%–2% sensor coverage while remaining scalable to three dimensions.
By Lihao Chen, Xinyu Zhang, Panqi Chen, Lei Cheng, Ting Zhang, Jianlong Li, Shikai Fang
arXiv:2609.07983v1 Announce Type: new
Abstract: Physics-Informed Neural Networks (PINNs) have recently emerged as a promising approach for solving Partial Differential Equations (PDEs), offering a me...
By Davide Staub, Ben Moseley
arXiv:2510. 25306v3 Announce Type: replace Abstract: Partial physical knowledge--governing structures known, constitutive relations or their combinations not--pervades spatiotemporal systems.
By Xizhe Wang, Xiaobin Song, Hongbo Zhao, Qingshan Jia, Qianchuan Zhao, Hao Sun, Benben Jiang
arXiv:2604. 07421v3 Announce Type: replace Abstract: Full-waveform inversion (FWI) is pivotal for reconstructing high-resolution subsurface velocity models but remains computationally intensive and ill-posed.
By Zhenyu Wang, Peiyuan Li, Yongxiang Shi, Ruoyu Wu, Chenfei Liao, Lei Zhang
arXiv:2607. 19147v1 Announce Type: cross Abstract: Data-driven methods have revolutionized ocean modeling, yet current approaches rely heavily on complete reanalysis datasets, imposing computational constraints and limiting model performance to that of the training data.
By Yangyang Kong, Yutong Jiang, Yanhai Gan, Junyu Dong, Feng Gao, Xiaopei Lin
arXiv:2507. 03094v2 Announce Type: replace-cross Abstract: Many challenges in scientific imaging involve solving ill-posed inverse problems, where the goal is to recover spatio-temporal fields from indirect, noisy, and highly sparse measurements - often without access to ground truth data or reliable simulators.
By Ali SaraerToosi, Renbo Tu, Esther Y. H. Lin, Kamyar Azizzadenesheli, Aviad Levis
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
arXiv:2601. 17074v4 Announce Type: replace-cross Abstract: Accurate estimation in time-varying inverse problems under limited and sparse observations remains a fundamental challenge across scientific domains.
By Akila Sampath, Vandana Janeja, Jianwu Wang
arXiv:2608.23215v1 Announce Type: cross
Abstract: Automated perception in side-scan sonar (SSS) imagery is severely hindered by physical acoustic artifacts, resulting in representations that inextric...
By Taqi Hamoda, Hayat Rajani, Nuno Gracias
Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning. Physics-based ocean forecasting models impose prohibitive computational costs,...
arXiv:2608. 16070v1 Announce Type: cross Abstract: Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning.
By Wei Wu, Xiang Wang, Hongze Leng, Qingye Min, Junxing Zhu, Junqiang Song