title: Fast neural network surrogates for very high dimensional physics-based models in computational 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(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.
authors.affiliation: Department of Computer Science and Electrical Engineering, OGI School of Science and Engineering, Oregon Health and Science University, Portland, OR 97006, USA.
authors.forename: Rudolph
authors.initials: R
authors.lastname: van der Merwe
authors.forename: Todd K
authors.initials: TK
authors.lastname: Leen
authors.forename: Zhengdong
authors.initials: Z
authors.lastname: Lu
authors.forename: Sergey
authors.initials: S
authors.lastname: Frolov
authors.forename: Antonio M
authors.initials: AM
authors.lastname: Baptista
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D005246:Feedback; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009791:Oceanography; D055585:Physical Phenomena; D010825:Physics; D045483:Rivers; D013057:Spectrum Analysis; D013997:Time Factors; D014887:Weather