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Research on surrogate model of dam numerical simulation with multiple outputs based on adaptive sampling
Dam numerical simulation is an important method to research the dam structural behavior, but it often takes a lot of time for calculation when facing problems that require many simulations, such as structural parameter back analysis. The surrogate model is widely used as a technology to reduce compu...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10366182/ https://www.ncbi.nlm.nih.gov/pubmed/37488144 http://dx.doi.org/10.1038/s41598-023-38590-z |
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author | Liang, Jiaming Li, Zhanchao Pan, Litan Khailah, Ebrahim Yahya Sun, Linsong Lu, Weigang |
author_facet | Liang, Jiaming Li, Zhanchao Pan, Litan Khailah, Ebrahim Yahya Sun, Linsong Lu, Weigang |
author_sort | Liang, Jiaming |
collection | PubMed |
description | Dam numerical simulation is an important method to research the dam structural behavior, but it often takes a lot of time for calculation when facing problems that require many simulations, such as structural parameter back analysis. The surrogate model is widely used as a technology to reduce computational cost. Although various methods have been widely investigated, there are still problems in designing the surrogate model's optimal Design of Experiments (DoE). In addition, most of the current DoE focuses on establishing a single-output problem. Designing a reasonable DoE for high-dimensional outputs is also a problem that needs to be solved. Based on the above issues, this research proposes a sequential surrogate model based on the radial basis function model (RBFM) with multi-outputs adaptive sampling. The benchmark function demonstrates the applicability of the proposed method to single-input & multi-outputs and multi-inputs & multi-outputs problems. Then, this method is applied to establishing a surrogate model for dam numerical simulation with multi-outputs. The result demonstrates that the proposed technique can be sampled adaptively and samples can be targeted based on the function form of the surrogate model, which significantly reduces the required sampling and calculation cost. |
format | Online Article Text |
id | pubmed-10366182 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103661822023-07-26 Research on surrogate model of dam numerical simulation with multiple outputs based on adaptive sampling Liang, Jiaming Li, Zhanchao Pan, Litan Khailah, Ebrahim Yahya Sun, Linsong Lu, Weigang Sci Rep Article Dam numerical simulation is an important method to research the dam structural behavior, but it often takes a lot of time for calculation when facing problems that require many simulations, such as structural parameter back analysis. The surrogate model is widely used as a technology to reduce computational cost. Although various methods have been widely investigated, there are still problems in designing the surrogate model's optimal Design of Experiments (DoE). In addition, most of the current DoE focuses on establishing a single-output problem. Designing a reasonable DoE for high-dimensional outputs is also a problem that needs to be solved. Based on the above issues, this research proposes a sequential surrogate model based on the radial basis function model (RBFM) with multi-outputs adaptive sampling. The benchmark function demonstrates the applicability of the proposed method to single-input & multi-outputs and multi-inputs & multi-outputs problems. Then, this method is applied to establishing a surrogate model for dam numerical simulation with multi-outputs. The result demonstrates that the proposed technique can be sampled adaptively and samples can be targeted based on the function form of the surrogate model, which significantly reduces the required sampling and calculation cost. Nature Publishing Group UK 2023-07-24 /pmc/articles/PMC10366182/ /pubmed/37488144 http://dx.doi.org/10.1038/s41598-023-38590-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Liang, Jiaming Li, Zhanchao Pan, Litan Khailah, Ebrahim Yahya Sun, Linsong Lu, Weigang Research on surrogate model of dam numerical simulation with multiple outputs based on adaptive sampling |
title | Research on surrogate model of dam numerical simulation with multiple outputs based on adaptive sampling |
title_full | Research on surrogate model of dam numerical simulation with multiple outputs based on adaptive sampling |
title_fullStr | Research on surrogate model of dam numerical simulation with multiple outputs based on adaptive sampling |
title_full_unstemmed | Research on surrogate model of dam numerical simulation with multiple outputs based on adaptive sampling |
title_short | Research on surrogate model of dam numerical simulation with multiple outputs based on adaptive sampling |
title_sort | research on surrogate model of dam numerical simulation with multiple outputs based on adaptive sampling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10366182/ https://www.ncbi.nlm.nih.gov/pubmed/37488144 http://dx.doi.org/10.1038/s41598-023-38590-z |
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