Stochastic Emulation for Efficient Onshore Probabilistic Tsunami Hazard Assessment

Abstract Machine learning is emerging as a promising strategy for modeling tsunami inundation at reduced computational cost. To enable probabilistic outputs that capture emulator uncertainty, we employ an ensemble of stochastic encoder‐decoder emulators with dropout‐based stochastic forward pass. Tr...

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Bibliografiske detaljer

Principais autores:	Naveen Ragu Ramalingam, Alice Abbate, Erlend Briseid Storrøsten, Gareth Davies, Stefano Lorito, Manuela Volpe, Steven J. Gibbons, Fabrizio Romano, Finn Løvholt
Format:	Artigo
Sprog:	Inglês
Udgivet:	Wiley 2026-04-01
Serier:	Journal of Geophysical Research: Machine Learning and Computation
Fag:	machine learning tsunami inundation stochastic emulation probabilistic tsunami hazard assessment (PTHA) encoder‐decoders uncertainity quantification
Online adgang:	https://doi.org/10.1029/2025JH001100
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