arXiv:2512. 19804v2 Announce Type: replace Abstract: Reduced-order models (ROMs) can represent spatiotemporal processes in significantly fewer dimensions and can often be solved many orders of magnitude faster than their governing partial differential equations (PDEs).
arXiv:2606. 05618v1 Announce Type: cross Abstract: Extreme events -- such as earthquakes and coronal mass ejections -- are common in many chaotic dynamical systems, yet are difficult to characterize and predict due to the subtle instability mechanisms that drive them.
arXiv:2609.37565v1 Announce Type: new Abstract: Deep learning surrogates can produce high-resolution coastal flood maps orders of magnitude faster than physics-based hydrodynamic simulators, yet tran...
arXiv:2506. 04281v2 Announce Type: replace Abstract: Compound flooding, driven by nonlinear interactions between multiple hydrometeorological factors, poses a significant challenge to hazard prevention.
arXiv:2608. 04327v1 Announce Type: new Abstract: Explicit onshore tsunami inundation forecasting can improve public risk awareness, but deterministically predicted inundation boundaries under highly uncertain conditions, such as near-field tsunamis generated by megathrust earthquakes, may falsely imply safety outside the boundaries.
arXiv:2609.13242v1 Announce Type: cross Abstract: Subgrid parametrizations of vertical mixing in ocean models depend on free coefficients that cannot be measured directly and must be calibrated again...
arXiv:2605. 20494v2 Announce Type: replace Abstract: Reliable assessment of tropical cyclone risk is limited by the short and spatially uneven historical record, especially for rare, high-intensity landfalls that dominate insured loss.
SurgeGen is a two‑stage generative diffusion framework that synthesizes storm surge scenarios conditioned on continuous storm parameters. The first stage produces a coarse baseline surge estimate, which then conditions a diffusion model that refines the output to capture realistic spatial patterns and variability. The method can generate diverse, realistic surge scenarios both within and beyond the training distribution.
The paper introduces Sensitivity‑Constrained Neural Operators (SC‑NOs), which augment standard neural operator training with sampled Jacobian supervision from differentiable solvers or discrete adjoints. By matching selected sensitivities during training, SC‑NOs improve forward prediction accuracy and significantly enhance gradient‑based inverse reconstruction for distributed fields. Experiments on advection–diffusion, RANS–Spalart–Allmaras, high‑dimensional gridded inputs, and a shallow‑water tsunami source‑inversion case demonstrate that SC‑NOs achieve a better accuracy–cost trade‑off and enable near‑real‑time wave‑propagation forecasting from sparse observations.
arXiv:2607. 05127v1 Announce Type: cross Abstract: Understanding the physics of many-body complex dynamical systems is typically non-trivial.
MorphoGP is a nonparametric Gaussian process framework designed to predict equilibrium beach profiles under tidal influence. It first classifies beach morphologies using a ContourCluster model based on contrastive learning, then trains a specialized Gaussian process expert for each category to learn relationships between environmental descriptors (waves, tides, sediments) and beach shape. A Gating Net probabilistically combines the experts’ outputs, achieving a 59.3% reduction in test RMSE compared to the best baseline, with a final RMSE of 0.297 m on over 180 Chinese coast beach profiles.
arXiv:2606. 11140v1 Announce Type: cross Abstract: Data assimilation (DA) in subsurface flow entails calibrating model parameters to match observed data, typically at wells, while preserving geological realism.
arXiv:2606. 26389v1 Announce Type: cross Abstract: Sea state prediction is essential for operational maritime applications and coupled earth system modeling, yet current spectral wave models remain computationally prohibitive for many use cases, including online coupling to climate simulations and making probabilistic (ensemble-based) predictions.