Evaluation of POD based surrogate models of fields resulting from nonlinear FEM simulations

Abstract Surrogate modelling is a powerful tool to replace computationally expensive nonlinear numerical simulations, with fast representations thereof, for inverse analysis, model-based control or optimization. For some problems, it is required that the surrogate model describes a complete output f...

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Autores principales:	Boukje M. de Gooijer, Jos Havinga, Hubert J. M. Geijselaers, Anton H. van den Boogaard
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Metamodel Multiphysical field Preprocessing Proper Orthogonal Decomposition Radial Basis Function Mechanics of engineering. Applied mechanics TA349-359 Systems engineering TA168
Acceso en línea:	https://doaj.org/article/8ed529ca6f9c4bddad486e854f74f5a6
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spelling	oai:doaj.org-article:8ed529ca6f9c4bddad486e854f74f5a62021-11-07T12:18:06ZEvaluation of POD based surrogate models of fields resulting from nonlinear FEM simulations10.1186/s40323-021-00210-82213-7467https://doaj.org/article/8ed529ca6f9c4bddad486e854f74f5a62021-11-01T00:00:00Zhttps://doi.org/10.1186/s40323-021-00210-8https://doaj.org/toc/2213-7467Abstract Surrogate modelling is a powerful tool to replace computationally expensive nonlinear numerical simulations, with fast representations thereof, for inverse analysis, model-based control or optimization. For some problems, it is required that the surrogate model describes a complete output field. To construct such surrogate models, proper orthogonal decomposition (POD) can be used to reduce the dimensionality of the output data. The accuracy of the surrogate models strongly depends on the (pre)processing actions that are used to prepare the data for the dimensionality reduction. In this work, POD-based surrogate models with Radial Basis Function interpolation are used to model high-dimensional FE data fields. The effect of (pre)processing methods on the accuracy of the result field is systematically investigated. Different existing methods for surrogate model construction are compared with a novel method. Special attention is given to data fields consisting of several physical meanings, e.g. displacement, strain and stress. A distinction is made between the errors due to truncation and due to interpolation of the data. It is found that scaling the data per physical part substantially increases the accuracy of the surrogate model.Boukje M. de GooijerJos HavingaHubert J. M. GeijselaersAnton H. van den BoogaardSpringerOpenarticleMetamodelMultiphysical fieldPreprocessingProper Orthogonal DecompositionRadial Basis FunctionMechanics of engineering. Applied mechanicsTA349-359Systems engineeringTA168ENAdvanced Modeling and Simulation in Engineering Sciences, Vol 8, Iss 1, Pp 1-33 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Metamodel Multiphysical field Preprocessing Proper Orthogonal Decomposition Radial Basis Function Mechanics of engineering. Applied mechanics TA349-359 Systems engineering TA168
spellingShingle	Metamodel Multiphysical field Preprocessing Proper Orthogonal Decomposition Radial Basis Function Mechanics of engineering. Applied mechanics TA349-359 Systems engineering TA168 Boukje M. de Gooijer Jos Havinga Hubert J. M. Geijselaers Anton H. van den Boogaard Evaluation of POD based surrogate models of fields resulting from nonlinear FEM simulations
description	Abstract Surrogate modelling is a powerful tool to replace computationally expensive nonlinear numerical simulations, with fast representations thereof, for inverse analysis, model-based control or optimization. For some problems, it is required that the surrogate model describes a complete output field. To construct such surrogate models, proper orthogonal decomposition (POD) can be used to reduce the dimensionality of the output data. The accuracy of the surrogate models strongly depends on the (pre)processing actions that are used to prepare the data for the dimensionality reduction. In this work, POD-based surrogate models with Radial Basis Function interpolation are used to model high-dimensional FE data fields. The effect of (pre)processing methods on the accuracy of the result field is systematically investigated. Different existing methods for surrogate model construction are compared with a novel method. Special attention is given to data fields consisting of several physical meanings, e.g. displacement, strain and stress. A distinction is made between the errors due to truncation and due to interpolation of the data. It is found that scaling the data per physical part substantially increases the accuracy of the surrogate model.
format	article
author	Boukje M. de Gooijer Jos Havinga Hubert J. M. Geijselaers Anton H. van den Boogaard
author_facet	Boukje M. de Gooijer Jos Havinga Hubert J. M. Geijselaers Anton H. van den Boogaard
author_sort	Boukje M. de Gooijer
title	Evaluation of POD based surrogate models of fields resulting from nonlinear FEM simulations
title_short	Evaluation of POD based surrogate models of fields resulting from nonlinear FEM simulations
title_full	Evaluation of POD based surrogate models of fields resulting from nonlinear FEM simulations
title_fullStr	Evaluation of POD based surrogate models of fields resulting from nonlinear FEM simulations
title_full_unstemmed	Evaluation of POD based surrogate models of fields resulting from nonlinear FEM simulations
title_sort	evaluation of pod based surrogate models of fields resulting from nonlinear fem simulations
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/8ed529ca6f9c4bddad486e854f74f5a6
work_keys_str_mv	AT boukjemdegooijer evaluationofpodbasedsurrogatemodelsoffieldsresultingfromnonlinearfemsimulations AT joshavinga evaluationofpodbasedsurrogatemodelsoffieldsresultingfromnonlinearfemsimulations AT hubertjmgeijselaers evaluationofpodbasedsurrogatemodelsoffieldsresultingfromnonlinearfemsimulations AT antonhvandenboogaard evaluationofpodbasedsurrogatemodelsoffieldsresultingfromnonlinearfemsimulations
_version_	1718443501384892416

Evaluation of POD based surrogate models of fields resulting from nonlinear FEM simulations

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