Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models
文献类型：期刊论文
| 作者 | Guo, Ling2; Wu, Hao3; Zhou, Tao1 |
| 刊名 | JOURNAL OF COMPUTATIONAL PHYSICS
 |
| 出版日期 | 2022-07-15 |
| 卷号 | 461页码:18 |
| 关键词 | Data -driven modeling Normalizing flows Uncertainty quantification Random fields |
| ISSN号 | 0021-9991 |
| DOI | 10.1016/j.jcp.2022.111202 |
| 英文摘要 | We introduce in this work the normalizing field flows (NFF) for learning random fields from scattered measurements. More precisely, we construct a bijective transformation (a normalizing flow characterizing by neural networks) between a Gaussian random field with the Karhunen-Loeve (KL) expansion structure and the target stochastic field, where the KL expansion coefficients and the invertible networks are trained by maximizing the sum of the log-likelihood on scattered measurements. This NFF model can be used to solve data-driven forward, inverse, and mixed forward/inverse stochastic partial differential equations in a unified framework. We demonstrate the capability of the proposed NFF model for learning non-Gaussian processes and different types of stochastic partial differential equations. (C)& nbsp;2022 Elsevier Inc. All rights reserved. |
| 资助项目 | NSF of China[12071301] ; NSF of China[11671265] ; NSF of China[11822111] ; NSF of China[2020YFA0712000] ; NSF of China[XDA25010404] ; Shanghai Municipal Science and Technology Commission[12171367] ; Shanghai Municipal Science and Technology Commission[11688101] ; Shanghai Municipal Science and Technology Commission[20JC1412500] ; Shanghai Municipal Science and Technology Commission[20JC1413500] ; National Key R&D Program of China[21JC1403700] ; Strategic Priority Research Program of Chinese Academy of Sciences[2021SHZDZX0100] |
| WOS研究方向 | Computer Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000802129600002 |
| 出版者 | ACADEMIC PRESS INC ELSEVIER SCIENCE |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/61517]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Wu, Hao |
| 作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Computat Math & Sci Engn Comp, LSEC, Beijing, Peoples R China 2.Shanghai Normal Univ, Dept Math, Shanghai, Peoples R China 3.Tongji Univ, Sch Math Sci, Shanghai, Peoples R China |
| 推荐引用方式 GB/T 7714 |
Guo, Ling,Wu, Hao,Zhou, Tao. Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models [J]. JOURNAL OF COMPUTATIONAL PHYSICS,2022,461:18. |
| APA |
Guo, Ling,Wu, Hao,&Zhou, Tao.(2022). Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models .JOURNAL OF COMPUTATIONAL PHYSICS,461,18. |
| MLA |
Guo, Ling,et al." Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models ".JOURNAL OF COMPUTATIONAL PHYSICS 461(2022):18. |
入库方式： OAI收割
来源：数学与系统科学研究院
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