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

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.

Authors:
 [1]; ORCiD logo [1];  [2]
  1. Oklahoma State Univ., Stillwater, OK (United States)
  2. SINTEF Digital, Trondheim (Norway)
Publication Date:
Research Org.:
Oklahoma State Univ., Stillwater, OK (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
OSTI Identifier:
1593573
Grant/Contract Number:  
SC0019290
Resource Type:
Accepted Manuscript
Journal Name:
Fluids
Additional Journal Information:
Journal Volume: 3; Journal Issue: 4; Journal ID: ISSN 2311-5521
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; quasi-geostrophic ocean model; hybrid modeling; extreme learning machine; proper orthogonal decomposition; Galerkin projection

Citation Formats

Rahman, Sk., San, Omer, and Rasheed, Adil. A Hybrid Approach for Model Order Reduction of Barotropic Quasi-Geostrophic Turbulence. United States: N. p., 2018. Web. doi:10.3390/fluids3040086.
Rahman, Sk., San, Omer, & Rasheed, Adil. A Hybrid Approach for Model Order Reduction of Barotropic Quasi-Geostrophic Turbulence. United States. https://doi.org/10.3390/fluids3040086
Rahman, Sk., San, Omer, and Rasheed, Adil. Wed . "A Hybrid Approach for Model Order Reduction of Barotropic Quasi-Geostrophic Turbulence". United States. https://doi.org/10.3390/fluids3040086. https://www.osti.gov/servlets/purl/1593573.
@article{osti_1593573,
title = {A Hybrid Approach for Model Order Reduction of Barotropic Quasi-Geostrophic Turbulence},
author = {Rahman, Sk. and San, Omer and Rasheed, Adil},
abstractNote = {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.},
doi = {10.3390/fluids3040086},
journal = {Fluids},
number = 4,
volume = 3,
place = {United States},
year = {Wed Oct 31 00:00:00 EDT 2018},
month = {Wed Oct 31 00:00:00 EDT 2018}
}

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Works referencing / citing this record:

A dynamic closure modeling framework for model order reduction of geophysical flows
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Memory embedded non-intrusive reduced order modeling of non-ergodic flows
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