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Fast neural network surrogates for very high dimensional physics-based models in computational oceanography

TitleFast neural network surrogates for very high dimensional physics-based models in computational oceanography
Publication TypeJournal Article
Year of Publication2007
Authorsvan der Merwe R, Leen TK, Lu Z, Frolov S, Baptista AM
Journal TitleNeural Networks
Volume20
Issue4
Pages462 - 478
ISSN08936080
Keywordscomputational oceanography
Abstract

We present neural network surrogates that provide extremely fast and accurate emulation of a large-scale circulation model for the coupled Columbia River, its estuary and near ocean regions. The circulation model has O (107) degrees of freedom, is highly nonlinear and is driven by ocean, atmospheric and river influences at its boundaries. The surrogates provide accurate emulation of the full circulation code and run over 1000 times faster. Such fast dynamic surrogates will enable significant advances in ensemble forecasts in oceanography and weather.

DOI10.1016/j.neunet.2007.04.023