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).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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

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arXiv Machine Learning

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arXiv Machine Learning

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