2007 Special Issue: Fast neural network surrogates for very high dimensional physics-based models in computational oceanography

Authors:
Rudolph van der Merwe;Todd K. Leen;Zhengdong Lu;Sergey Frolov;Antonio M. Baptista
Affiliations:
Department of Computer Science and Electrical Engineering, OGI School of Science and Engineering, Oregon Health and Science University, Portland, OR 97006, USA;Department of Computer Science and Electrical Engineering, OGI School of Science and Engineering, Oregon Health and Science University, Portland, OR 97006, USA;Department of Computer Science and Electrical Engineering, OGI School of Science and Engineering, Oregon Health and Science University, Portland, OR 97006, USA;Department of Environmental and Biomolecular Systems, OGI School of Science and Engineering, Oregon Health and Science University, Portland, OR 97006, USA;Department of Environmental and Biomolecular Systems, OGI School of Science and Engineering, Oregon Health and Science University, Portland, OR 97006, USA
Venue:
Neural Networks
Year:
2007

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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(10^7) 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.