An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis
To quantify the uncertainties in multi-dimensional flow field correlated responses caused by uncertain model parameters, this paper presents an adaptive multi-fidelity model based on gappy proper orthogonal decomposition (Gappy-POD), which integrates the two conventional approaches for enhancing the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/6/3359 |
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| author | Wei Xiao Yingying Shen Jiao Zhao Luogeng Lv Jiangtao Chen Wei Zhao |
| author_facet | Wei Xiao Yingying Shen Jiao Zhao Luogeng Lv Jiangtao Chen Wei Zhao |
| author_sort | Wei Xiao |
| collection | DOAJ |
| description | To quantify the uncertainties in multi-dimensional flow field correlated responses caused by uncertain model parameters, this paper presents an adaptive multi-fidelity model based on gappy proper orthogonal decomposition (Gappy-POD), which integrates the two conventional approaches for enhancing the efficiency of surrogate modeling, namely, multi-fidelity modeling and adaptive sampling algorithms. The challenges surrounding the selection of initial high-fidelity samples and the subsequent incremental augmentation of these samples are addressed. The <i>k</i>-means clustering algorithm is employed to identify locations within the parameter space for conducting high-fidelity simulations, leveraging insights gained from low-fidelity responses. An adaptive sampling criterion, leveraging the low-fidelity projection error derived from the Gappy-POD method, is implemented to progressively augment high-fidelity samples. The results demonstrate that the adaptive model consistently outperforms random sampling methods, highlighting its superiority in terms of accuracy and reliability, providing an efficient and reliable prediction model for uncertainty quantification. |
| format | Article |
| id | doaj-art-40563a78aaa6404e8b8af8e50256993c |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-40563a78aaa6404e8b8af8e50256993c2025-08-20T02:11:08ZengMDPI AGApplied Sciences2076-34172025-03-01156335910.3390/app15063359An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation AnalysisWei Xiao0Yingying Shen1Jiao Zhao2Luogeng Lv3Jiangtao Chen4Wei Zhao5China Aerodynamics Research and Development Center, Mianyang 621000, ChinaChina Aerodynamics Research and Development Center, Mianyang 621000, ChinaChina Aerodynamics Research and Development Center, Mianyang 621000, ChinaChina Aerodynamics Research and Development Center, Mianyang 621000, ChinaChina Aerodynamics Research and Development Center, Mianyang 621000, ChinaChina Aerodynamics Research and Development Center, Mianyang 621000, ChinaTo quantify the uncertainties in multi-dimensional flow field correlated responses caused by uncertain model parameters, this paper presents an adaptive multi-fidelity model based on gappy proper orthogonal decomposition (Gappy-POD), which integrates the two conventional approaches for enhancing the efficiency of surrogate modeling, namely, multi-fidelity modeling and adaptive sampling algorithms. The challenges surrounding the selection of initial high-fidelity samples and the subsequent incremental augmentation of these samples are addressed. The <i>k</i>-means clustering algorithm is employed to identify locations within the parameter space for conducting high-fidelity simulations, leveraging insights gained from low-fidelity responses. An adaptive sampling criterion, leveraging the low-fidelity projection error derived from the Gappy-POD method, is implemented to progressively augment high-fidelity samples. The results demonstrate that the adaptive model consistently outperforms random sampling methods, highlighting its superiority in terms of accuracy and reliability, providing an efficient and reliable prediction model for uncertainty quantification.https://www.mdpi.com/2076-3417/15/6/3359adaptive samplingmulti-fidelity modelmulti-dimensional correlated responsesmachine learningflow field reduction |
| spellingShingle | Wei Xiao Yingying Shen Jiao Zhao Luogeng Lv Jiangtao Chen Wei Zhao An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis Applied Sciences adaptive sampling multi-fidelity model multi-dimensional correlated responses machine learning flow field reduction |
| title | An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis |
| title_full | An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis |
| title_fullStr | An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis |
| title_full_unstemmed | An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis |
| title_short | An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis |
| title_sort | adaptive multi fidelity surrogate model for uncertainty propagation analysis |
| topic | adaptive sampling multi-fidelity model multi-dimensional correlated responses machine learning flow field reduction |
| url | https://www.mdpi.com/2076-3417/15/6/3359 |
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