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

Rudolph van der Merwe, Todd K. Leen, Zhengdong Lu, Sergey Frolov, Antonio Baptista

Research output: Contribution to journalArticle

38 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)462-478
Number of pages17
JournalNeural Networks
Volume20
Issue number4
DOIs
StatePublished - May 2007

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Oceanography
Physics
Rivers
Oceans and Seas
Neural networks
Estuaries
Weather

Keywords

  • Computational oceanography
  • Data assimilation
  • Fast neural network dynamic surrogates
  • High-dimensional time series prediction
  • Physics-based models
  • River estuary modelling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Neuroscience(all)

Cite this

Fast neural network surrogates for very high dimensional physics-based models in computational oceanography. / van der Merwe, Rudolph; Leen, Todd K.; Lu, Zhengdong; Frolov, Sergey; Baptista, Antonio.

In: Neural Networks, Vol. 20, No. 4, 05.2007, p. 462-478.

Research output: Contribution to journalArticle

van der Merwe, Rudolph ; Leen, Todd K. ; Lu, Zhengdong ; Frolov, Sergey ; Baptista, Antonio. / Fast neural network surrogates for very high dimensional physics-based models in computational oceanography. In: Neural Networks. 2007 ; Vol. 20, No. 4. pp. 462-478.
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