Parallel ensemble Kalman method with total variation regularization for large-scale field inversion
文献类型：期刊论文
| 作者 | Zhang XL(张鑫磊); Zhang L(张磊); He GW(何国威)
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| 刊名 | JOURNAL OF COMPUTATIONAL PHYSICS
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| 出版日期 | 2024-07-15 |
| 卷号 | 509页码:18 |
| 关键词 | Ensemble Kalman method Parallel implementation Field inversion Data assimilation Machine learning |
| ISSN号 | 0021-9991 |
| DOI | 10.1016/j.jcp.2024.113059 |
| 通讯作者 | He, Guowei(hgw@lnm.imech.ac.cn) |
| 英文摘要 | Field inversion is often encountered in data -driven computational modeling to infer latent spatial- varying parameters from available observations. The ensemble Kalman method is emerging as a useful tool for solving field inversion problems due to its derivative -free merits. However, the method is computationally prohibitive for large-scale field inversion with high -dimensional observation data, which necessitates developing a practical efficient implementation strategy. In this work, we propose a parallel implementation of the ensemble Kalman method with total variation regularization for large-scale field inversion problems. It is achieved by partitioning the computational domain into non -overlapping subdomains and performing local ensemble Kalman updates at each subdomain parallelly. In doing so, the computational complexity of the ensemblebased inversion method is significantly reduced to the level of local subdomains. Further, the total variation regularization is employed to smoothen the physical field over the entire domain, which can reduce the inference discrepancy caused by missing covariances near subdomain interfaces. The capability of the proposed method is demonstrated in three field inversion problems with increasing complexity, i.e., the diffusion problem, the scalar transport problem and the Reynolds averaged Navier-Stokes closure problem. The numerical results show that the proposed method can significantly improve computational efficiency with satisfactory inference accuracy. |
| 分类号 | 一类/力学重要期刊 |
| WOS关键词 | DATA-DRIVEN |
| 资助项目 | NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics[11988102] ; National Natural Science Foundation of China[12102435] ; China Post-doctoral Science Foundation[2021M690154] ; Young Elite Scientists Sponsorship Program by CAST[2022QNRC001] |
| WOS研究方向 | Computer Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001239659300001 |
| 资助机构 | NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics ; National Natural Science Foundation of China ; China Post-doctoral Science Foundation ; Young Elite Scientists Sponsorship Program by CAST |
| 其他责任者 | He, Guowei |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/95572]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 推荐引用方式 GB/T 7714 | Zhang XL,Zhang L,He GW. Parallel ensemble Kalman method with total variation regularization for large-scale field inversion[J]. JOURNAL OF COMPUTATIONAL PHYSICS,2024,509:18. |
| APA | 张鑫磊,张磊,&何国威.(2024).Parallel ensemble Kalman method with total variation regularization for large-scale field inversion.JOURNAL OF COMPUTATIONAL PHYSICS,509,18. |
| MLA | 张鑫磊,et al."Parallel ensemble Kalman method with total variation regularization for large-scale field inversion".JOURNAL OF COMPUTATIONAL PHYSICS 509(2024):18. |
入库方式： OAI收割
来源：力学研究所
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