arXiv Machine Learning August 14, 2026 By Shane X. Coffing, John Tipton, Arvind T. Mohan, Darren Engwirda

Reduced Order Modeling for Tsunami Forecasting with Bayesian Hierarchical Pooling

Read the original on arXiv Machine Learning →

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).

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

arXiv Machine Learning

Jun 5

Uncovering Extreme Event Mechanisms for Prediction and Control with Sensitivity-Balanced Projections

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.

By Nicholas Zolman, Sajeda Mokbel, Samuel E. Otto, Steven L. Brunton

arXiv Machine Learning

Jun 4

Uncovering Insights of Compound Flooding with Data-Driven AI

arXiv:2506. 04281v2 Announce Type: replace Abstract: Compound flooding, driven by nonlinear interactions between multiple hydrometeorological factors, poses a significant challenge to hazard prevention.

By Xu Zheng, Chaohao Lin, Sipeng Chen, Zhuomin Chen, Jimeng Shi, Jayantha Obeysekera, Jingchao Ni, Wei Cheng, Jason Liu, Dongsheng Luo

arXiv Machine Learning

Aug 6

Real-time probabilistic tsunami forecasting via generative AI

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.

By Yusuke Oishi, Takashi Furumura, Fumihiko Imamura

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arXiv Machine Learning

Jul 7

A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)

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.

By Jennifer Nakamura, Upmanu Lall

safety

arXiv Machine Learning

Jul 7

Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spaces

arXiv:2607. 05127v1 Announce Type: cross Abstract: Understanding the physics of many-body complex dynamical systems is typically non-trivial.

By Domiziano Doria, Matteo Becchi, Giovanni M. Pavan

arXiv AI

Jun 10

Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniques

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.

By Guido Di Federico, Wenchao Teng, Louis J. Durlofsky

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Related stories

Uncovering Extreme Event Mechanisms for Prediction and Control with Sensitivity-Balanced Projections

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Real-time probabilistic tsunami forecasting via generative AI

A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)

Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spaces

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