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Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites

High-fidelity structural analysis using numerical techniques, such as finite element method (FEM), has become an essential step in design of laminated composite structures. Despite its high accuracy, the computational intensiveness of FEM is its serious drawback. Once trained properly, the metamodel...

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Autores principales: Kalita, Kanak, Chakraborty, Shankar, Madhu, S, Ramachandran, Manickam, Gao, Xiao-Zhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232655/
https://www.ncbi.nlm.nih.gov/pubmed/34203794
http://dx.doi.org/10.3390/ma14123306
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author Kalita, Kanak
Chakraborty, Shankar
Madhu, S
Ramachandran, Manickam
Gao, Xiao-Zhi
author_facet Kalita, Kanak
Chakraborty, Shankar
Madhu, S
Ramachandran, Manickam
Gao, Xiao-Zhi
author_sort Kalita, Kanak
collection PubMed
description High-fidelity structural analysis using numerical techniques, such as finite element method (FEM), has become an essential step in design of laminated composite structures. Despite its high accuracy, the computational intensiveness of FEM is its serious drawback. Once trained properly, the metamodels developed with even a small training set of FEM data can be employed to replace the original FEM model. In this paper, an attempt is put forward to investigate the utility of radial basis function (RBF) metamodels in the predictive modelling of laminated composites. The effectiveness of various RBF basis functions is assessed. The role of problem dimensionality on the RBF metamodels is studied while considering a low-dimensional (2-variable) and a high-dimensional (16-variable) problem. The effect of uniformity of training sample points on the performance of RBF metamodels is also explored while considering three different sampling methods, i.e., random sampling, Latin hypercube sampling and Hammersley sampling. It is observed that relying only on the performance metrics, such as cross-validation error that essentially reuses training samples to assess the performance of the metamodels, may lead to ill-informed decisions. The performance of metamodels should also be assessed based on independent test data. It is further revealed that uniformity in training samples would lead towards better trained metamodels.
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spelling pubmed-82326552021-06-26 Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites Kalita, Kanak Chakraborty, Shankar Madhu, S Ramachandran, Manickam Gao, Xiao-Zhi Materials (Basel) Article High-fidelity structural analysis using numerical techniques, such as finite element method (FEM), has become an essential step in design of laminated composite structures. Despite its high accuracy, the computational intensiveness of FEM is its serious drawback. Once trained properly, the metamodels developed with even a small training set of FEM data can be employed to replace the original FEM model. In this paper, an attempt is put forward to investigate the utility of radial basis function (RBF) metamodels in the predictive modelling of laminated composites. The effectiveness of various RBF basis functions is assessed. The role of problem dimensionality on the RBF metamodels is studied while considering a low-dimensional (2-variable) and a high-dimensional (16-variable) problem. The effect of uniformity of training sample points on the performance of RBF metamodels is also explored while considering three different sampling methods, i.e., random sampling, Latin hypercube sampling and Hammersley sampling. It is observed that relying only on the performance metrics, such as cross-validation error that essentially reuses training samples to assess the performance of the metamodels, may lead to ill-informed decisions. The performance of metamodels should also be assessed based on independent test data. It is further revealed that uniformity in training samples would lead towards better trained metamodels. MDPI 2021-06-15 /pmc/articles/PMC8232655/ /pubmed/34203794 http://dx.doi.org/10.3390/ma14123306 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kalita, Kanak
Chakraborty, Shankar
Madhu, S
Ramachandran, Manickam
Gao, Xiao-Zhi
Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites
title Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites
title_full Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites
title_fullStr Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites
title_full_unstemmed Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites
title_short Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites
title_sort performance analysis of radial basis function metamodels for predictive modelling of laminated composites
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232655/
https://www.ncbi.nlm.nih.gov/pubmed/34203794
http://dx.doi.org/10.3390/ma14123306
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