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Error estimation of the parametric non-intrusive reduced order model using machine learning

Dunhui Xiao Orcid Logo

Computer Methods in Applied Mechanics and Engineering, Volume: 355, Pages: 513 - 534

Swansea University Author: Dunhui Xiao Orcid Logo

Abstract

A novel error estimation method for the parametric non-intrusive reduced order model (P-NIROM) based on machine learning is presented. This method relies on constructing a set of response functions for the errors between the high fidelity full model solutions and P-NIROM using machine learning metho...

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Published in: Computer Methods in Applied Mechanics and Engineering
ISSN: 0045-7825
Published: 2019
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa51023
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Abstract: A novel error estimation method for the parametric non-intrusive reduced order model (P-NIROM) based on machine learning is presented. This method relies on constructing a set of response functions for the errors between the high fidelity full model solutions and P-NIROM using machine learning method, particularly, Gaussian process regression method. This yields closer solutions agreement with the high fidelity full model. The novelty of this work is that it is the first time to use machine learning method to derive error estimate for the P-NIROM. The capability of the new error estimation method is demonstrated using three numerical simulation examples: flow past a cylinder, dam break and 3D fluvial channel. It is shown that the results are closer to those of the high fidelity full model when considering error terms. In addition, the interface between two phases of dam break case is captured well if the error estimator is involved in the P-NIROM.
Keywords: NIROM, Machine learning, Gaussian process regression, Error estimation
College: Faculty of Science and Engineering
Start Page: 513
End Page: 534