Local and global timeseries proxies using functional principal component analysis: application to history-matching and uncertainty quantification

IF 2.1 3区地球科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computational Geosciences Pub Date : 2023-09-09 DOI:10.1007/s10596-023-10252-z

Hamidreza Hamdi, Christopher R. Clarkson, Mario Costa Sousa

引用次数: 0

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使用功能主成分分析的局部和全局时间序列代理:应用于历史匹配和不确定性量化

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Geosciences 地学-地球科学综合

CiteScore

6.10

自引率

4.00%

发文量

审稿时长

6-12 weeks

期刊介绍： Computational Geosciences publishes high quality papers on mathematical modeling, simulation, numerical analysis, and other computational aspects of the geosciences. In particular the journal is focused on advanced numerical methods for the simulation of subsurface flow and transport, and associated aspects such as discretization, gridding, upscaling, optimization, data assimilation, uncertainty assessment, and high performance parallel and grid computing. Papers treating similar topics but with applications to other fields in the geosciences, such as geomechanics, geophysics, oceanography, or meteorology, will also be considered. The journal provides a platform for interaction and multidisciplinary collaboration among diverse scientific groups, from both academia and industry, which share an interest in developing mathematical models and efficient algorithms for solving them, such as mathematicians, engineers, chemists, physicists, and geoscientists.