arXiv AI
Wave Physics as Representation: Tensor-Structured Neural Reconstruction for Ocean Acoustic Fields
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arXiv Machine Learning
Oct 19
Gabor-Enhanced Physics-Informed Neural Networks for Fast Simulations of Acoustic Wavefields
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.
arXiv Machine Learning
Sep 2
HarmoCore: Functional Latent Diffusion for Sparse Reconstruction of Oscillatory Wave Fields
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.
arXiv Machine Learning
Sep 10
Solving the Elastic Wave Equation with Physics-Informed Neural Networks: A Robust and Critical Assessment
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...
arXiv Machine Learning
Jul 22
Hierarchical Physics-Embedded Learning for Partially Known Spatiotemporal Dynamics
arXiv:2510. 25306v3 Announce Type: replace Abstract: Partial physical knowledge--governing structures known, constitutive relations or their combinations not--pervades spatiotemporal systems.
arXiv Machine Learning
Jun 9
SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion
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.
arXiv AI
Jul 22
Incomplete Observations Boost Evolutionary Performance in Ocean Modeling
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.