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DOI | 10.1029/2018WR022676 |
Uncertainty Quantification for Subsurface Flow and Transport: Coping With Nonlinearity/Irregularity via Polynomial Chaos Surrogate and Machine Learning | |
Meng, J.; Li, H. | |
2018-10-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2018 |
卷号 | 54期号:10页码:7733-7751 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
英文摘要 | Subsurface flow and transport problems usually involve some degree of uncertainty. Polynomial chaos expansion can be used as surrogate of physical models for uncertainty quantification. However, a global model can hardly be found for model responses with strong nonlinearity or irregularity. In this study, we propose a novel approach by use of the classification method in machine learning, that is, supported vector machine, to cope with such nonlinearity/irregularity. Piecewise surrogate models are constructed in relatively smooth subdomains separated by the supported vector machine hyperplanes. We demonstrate the effectiveness of using the trained piecewise surrogate model in solute transport and two-phase flow problems in homogeneous and heterogeneous porous media. The numerical results are compared with standard global polynomial chaos expansion results and the Monte Carlo benchmark. The proposed nonintrusive approach is able to accurately quantify uncertainty, with much smaller computational efforts. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000450726000034 |
WOS关键词 | PROBABILISTIC COLLOCATION METHOD ; SPECTRAL REPRESENTATION ; POROUS-MEDIA ; EFFICIENT ; TRANSFORM ; CONVERGENCE ; SIMULATIONS ; MODEL |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/19984 |
专题 | 资源环境科学 |
作者单位 | Peking Univ, Dept Energy & Resources Engn, Coll Engn, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Meng, J.,Li, H.. Uncertainty Quantification for Subsurface Flow and Transport: Coping With Nonlinearity/Irregularity via Polynomial Chaos Surrogate and Machine Learning[J]. WATER RESOURCES RESEARCH,2018,54(10):7733-7751. |
APA | Meng, J.,&Li, H..(2018).Uncertainty Quantification for Subsurface Flow and Transport: Coping With Nonlinearity/Irregularity via Polynomial Chaos Surrogate and Machine Learning.WATER RESOURCES RESEARCH,54(10),7733-7751. |
MLA | Meng, J.,et al."Uncertainty Quantification for Subsurface Flow and Transport: Coping With Nonlinearity/Irregularity via Polynomial Chaos Surrogate and Machine Learning".WATER RESOURCES RESEARCH 54.10(2018):7733-7751. |
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