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Fluids 2018, 3(4), 86; https://doi.org/10.3390/fluids3040086

A Hybrid Approach for Model Order Reduction of Barotropic Quasi-Geostrophic Turbulence

1
School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK 74078, USA
2
CSE Group, Mathematics and Cybernetics, SINTEF Digital, NO-7465 Trondheim, Norway
*
Author to whom correspondence should be addressed.
Received: 1 October 2018 / Revised: 24 October 2018 / Accepted: 26 October 2018 / Published: 31 October 2018
(This article belongs to the Collection Geophysical Fluid Dynamics)
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Abstract

We put forth a robust reduced-order modeling approach for near real-time prediction of mesoscale flows. In our hybrid-modeling framework, we combine physics-based projection methods with neural network closures to account for truncated modes. We introduce a weighting parameter between the Galerkin projection and extreme learning machine models and explore its effectiveness, accuracy and generalizability. To illustrate the success of the proposed modeling paradigm, we predict both the mean flow pattern and the time series response of a single-layer quasi-geostrophic ocean model, which is a simplified prototype for wind-driven general circulation models. We demonstrate that our approach yields significant improvements over both the standard Galerkin projection and fully non-intrusive neural network methods with a negligible computational overhead. View Full-Text
Keywords: quasi-geostrophic ocean model; hybrid modeling; extreme learning machine; proper orthogonal decomposition; Galerkin projection quasi-geostrophic ocean model; hybrid modeling; extreme learning machine; proper orthogonal decomposition; Galerkin projection
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Rahman, S.M.; San, O.; Rasheed, A. A Hybrid Approach for Model Order Reduction of Barotropic Quasi-Geostrophic Turbulence. Fluids 2018, 3, 86.

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