| Optimal sensor placement for ensemble-based data assimilation using gradient-weighted class activation mapping | |
Xu ZY(许昭越)1,2; Wang SZ(王士召)1,2 ; Zhang XL(张鑫磊)1,2; He GW(何国威)1,2
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| Corresponding Author | Wang, Shizhao([email protected]) ; Zhang, Xin-Lei([email protected]) |
| Source Publication | JOURNAL OF COMPUTATIONAL PHYSICS
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| 2024-10-01 | |
| Volume | 514Pages:20 |
| ISSN | 0021-9991 |
| Abstract | We introduce an optimal sensor placement method using convolutional neural networks for ensemble -based data assimilation. The proposed method utilizes the gradient -weighted class activation mapping of the convolutional neural networks to identify important regions for assimilation processes. It is achieved by using the initial ensemble of samples for data assimilation as training data to construct a convolutional neural network -based surrogate model. In doing so, the method can estimate optimal sensor locations in an a priori manner, allowing for sensor placement before conducting data assimilation processing. Moreover, the gradientweighted class activation mapping is used to alleviate the effect of error accumulation during the backpropagation process through global averaging. Further, these observation sensors are incorporated to reconstruct mean turbulent flow fields based on the ensemble Kalman method. The proposed optimal sensor placement method is applied to two flow applications with complex geometries, i.e., flows around periodic hills and an axisymmetric body of revolution. Both cases demonstrate that the proposed method can significantly reduce the number of sensors without sacrificing the accuracy of the reconstructed flow field. |
| Keyword | Optimal sensor placement GradCAM CNN Ensemble Kalman method |
| DOI | 10.1016/j.jcp.2024.113224 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001264450400001 |
| WOS Keyword | NEURAL-NETWORK ; FLUID-FLOW ; MODEL ; OPTIMIZATION ; SIMULATION ; ALGORITHM ; SPARSE ; DRIVEN |
| WOS Research Area | Computer Science ; Physics |
| WOS Subject | Computer Science, Interdisciplinary Applications ; Physics, Mathematical |
| Funding Project | NSFC Basic Science Center Program for 'Multiscale Problems in Nonlinear Mechanics'[11988102] ; National Natural Science Foundation of China[92252203] ; National Natural Science Foundation of China[91952301] ; National Natural Science Foundation of China[12102435] ; CAS Project for Young Scientists in Basic Research[YSBR-087] ; CAST Young Elite Scientists Sponsorship Program[2022QNRC001] |
| Funding Organization | NSFC Basic Science Center Program for 'Multiscale Problems in Nonlinear Mechanics' ; National Natural Science Foundation of China ; CAS Project for Young Scientists in Basic Research ; CAST Young Elite Scientists Sponsorship Program |
| Classification | 一类/力学重要期刊 |
| Ranking | 1 |
| Contributor | Wang, Shizhao ; Zhang, Xin-Lei |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/95984 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.Chinese Acad Sci, LNM, Inst Mech, Beijing 100190, Peoples R China; 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China |
| Recommended Citation GB/T 7714 | Xu ZY,Wang SZ,Zhang XL,et al. Optimal sensor placement for ensemble-based data assimilation using gradient-weighted class activation mapping[J]. JOURNAL OF COMPUTATIONAL PHYSICS,2024,514:20.Rp_Au:Wang, Shizhao, Zhang, Xin-Lei |
| APA | 许昭越,王士召,张鑫磊,&何国威.(2024).Optimal sensor placement for ensemble-based data assimilation using gradient-weighted class activation mapping.JOURNAL OF COMPUTATIONAL PHYSICS,514,20. |
| MLA | 许昭越,et al."Optimal sensor placement for ensemble-based data assimilation using gradient-weighted class activation mapping".JOURNAL OF COMPUTATIONAL PHYSICS 514(2024):20. |
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