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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

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DOI (Published version): 10.1016/j.cma.2019.06.018

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
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URI: https://cronfa.swan.ac.uk/Record/cronfa51023
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first_indexed 2019-07-06T21:26:41Z
last_indexed 2020-10-27T04:02:58Z
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spelling 2020-10-26T12:50:50.3726226 v2 51023 2019-07-06 Error estimation of the parametric non-intrusive reduced order model using machine learning 62c69b98cbcdc9142622d4f398fdab97 0000-0003-2461-523X Dunhui Xiao Dunhui Xiao true false 2019-07-06 AERO 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. Journal Article Computer Methods in Applied Mechanics and Engineering 355 513 534 0045-7825 NIROM, Machine learning, Gaussian process regression, Error estimation 1 10 2019 2019-10-01 10.1016/j.cma.2019.06.018 COLLEGE NANME Aerospace Engineering COLLEGE CODE AERO Swansea University 2020-10-26T12:50:50.3726226 2019-07-06T16:03:40.4640597 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Dunhui Xiao 0000-0003-2461-523X 1 0051023-12072019155855.pdf xiao2019(2).pdf 2019-07-12T15:58:55.9830000 Output 1627155 application/pdf Accepted Manuscript true 2020-07-05T00:00:00.0000000 true eng
title Error estimation of the parametric non-intrusive reduced order model using machine learning
spellingShingle Error estimation of the parametric non-intrusive reduced order model using machine learning
Dunhui Xiao
title_short Error estimation of the parametric non-intrusive reduced order model using machine learning
title_full Error estimation of the parametric non-intrusive reduced order model using machine learning
title_fullStr Error estimation of the parametric non-intrusive reduced order model using machine learning
title_full_unstemmed Error estimation of the parametric non-intrusive reduced order model using machine learning
title_sort Error estimation of the parametric non-intrusive reduced order model using machine learning
author_id_str_mv 62c69b98cbcdc9142622d4f398fdab97
author_id_fullname_str_mv 62c69b98cbcdc9142622d4f398fdab97_***_Dunhui Xiao
author Dunhui Xiao
author2 Dunhui Xiao
format Journal article
container_title Computer Methods in Applied Mechanics and Engineering
container_volume 355
container_start_page 513
publishDate 2019
institution Swansea University
issn 0045-7825
doi_str_mv 10.1016/j.cma.2019.06.018
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
document_store_str 1
active_str 0
description 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.
published_date 2019-10-01T04:02:45Z
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score 11.036706

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