FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387642812> ?p ?o ?g. }

• W4387642812 endingPage "212404" @default.
• W4387642812 startingPage "212404" @default.
• W4387642812 abstract "This work aims to help improve the performance of an iterative ensemble smoother (IES) in reservoir data assimilation problems, by introducing a data-driven procedure to optimize the choice of certain algorithmic hyper-parameters in the IES. Generally speaking, algorithmic hyper-parameters exist in various data assimilation algorithms. Taking IES as an example, localization is often useful for improving its performance, yet applying localization to an IES also introduces a certain number of algorithmic hyper-parameters, such as localization length scales, in the course of data assimilation. While different methods have been developed in the literature to address the problem of properly choosing localization length scales in various circumstances, many of them are tailored to specific problems under consideration, and may be difficult to directly extend to other problems. In addition, conventional hyper-parameter tuning methods determine the values of localization length scales based on either empirical (e.g., using experience, domain knowledge, or simply the practice of trial and error) or analytic (e.g., through statistical analyses) rules, but few of them use the information of observations to optimize the choice of hyper-parameters. The current work proposes a generic, data-driven hyper-parameter tuning strategy that has the potential to overcome the aforementioned issues. With this proposed strategy, hyper-parameter optimization is converted into a conventional parameter estimation problem, in such a way that observations are utilized to guide the choice of hyper-parameters. One noticeable feature of the proposed hyper-parameter tuning strategy is that it iteratively estimates an ensemble of hyper-parameters. In doing so, the resulting hyper-parameter tuning procedure receives some practical benefits inherent to conventional ensemble data assimilation algorithms, including the nature of being derivative-free, the ability to provide uncertainty quantification to some extent, and the capacity to handle a large number of hyper-parameters. Through 2D and 3D case studies, it is shown that when the proposed hyper-parameter tuning strategy is applied to tune a set of localization length scales (up to the order of 103) in a parameterized localization scheme, superior data assimilation performance is obtained in comparison to an alternative hyper-parameter tuning strategy without utilizing the information of observations." @default.
• W4387642812 created "2023-10-15" @default.
• W4387642812 creator A5026523739 @default.
• W4387642812 creator A5029510051 @default.
• W4387642812 creator A5044198320 @default.
• W4387642812 creator A5076511812 @default.
• W4387642812 date "2023-10-01" @default.
• W4387642812 modified "2023-10-15" @default.
• W4387642812 title "Hyper-parameter optimization for improving the performance of localization in an iterative ensemble smoother" @default.
• W4387642812 cites W1976584025 @default.
• W4387642812 cites W1995262681 @default.
• W4387642812 cites W2006105001 @default.
• W4387642812 cites W2008179957 @default.
• W4387642812 cites W2018353504 @default.
• W4387642812 cites W2023669272 @default.
• W4387642812 cites W2030664127 @default.
• W4387642812 cites W2030774493 @default.
• W4387642812 cites W2035050240 @default.
• W4387642812 cites W2046372107 @default.
• W4387642812 cites W2058488613 @default.
• W4387642812 cites W2066615678 @default.
• W4387642812 cites W2072631273 @default.
• W4387642812 cites W2078327207 @default.
• W4387642812 cites W2079724595 @default.
• W4387642812 cites W2082278382 @default.
• W4387642812 cites W2084913894 @default.
• W4387642812 cites W2098955426 @default.
• W4387642812 cites W2138172828 @default.
• W4387642812 cites W2146803308 @default.
• W4387642812 cites W2158940042 @default.
• W4387642812 cites W2298906243 @default.
• W4387642812 cites W2547561522 @default.
• W4387642812 cites W2888241046 @default.
• W4387642812 cites W2912288582 @default.
• W4387642812 cites W2916289860 @default.
• W4387642812 cites W2969410691 @default.
• W4387642812 cites W2979032535 @default.
• W4387642812 cites W2979624694 @default.
• W4387642812 cites W2990558975 @default.
• W4387642812 cites W3048974111 @default.
• W4387642812 cites W3100417316 @default.
• W4387642812 cites W3104223502 @default.
• W4387642812 cites W3106020863 @default.
• W4387642812 cites W3143044750 @default.
• W4387642812 cites W4221113402 @default.
• W4387642812 cites W4229041408 @default.
• W4387642812 cites W4281657216 @default.
• W4387642812 doi "https://doi.org/10.1016/j.geoen.2023.212404" @default.
• W4387642812 hasPublicationYear "2023" @default.
• W4387642812 type Work @default.
• W4387642812 citedByCount "0" @default.
• W4387642812 crossrefType "journal-article" @default.
• W4387642812 hasAuthorship W4387642812A5026523739 @default.
• W4387642812 hasAuthorship W4387642812A5029510051 @default.
• W4387642812 hasAuthorship W4387642812A5044198320 @default.
• W4387642812 hasAuthorship W4387642812A5076511812 @default.
• W4387642812 hasBestOaLocation W43876428121 @default.
• W4387642812 hasConcept C11413529 @default.
• W4387642812 hasConcept C119857082 @default.
• W4387642812 hasConcept C121332964 @default.
• W4387642812 hasConcept C124101348 @default.
• W4387642812 hasConcept C126255220 @default.
• W4387642812 hasConcept C153294291 @default.
• W4387642812 hasConcept C154945302 @default.
• W4387642812 hasConcept C167928553 @default.
• W4387642812 hasConcept C24552861 @default.
• W4387642812 hasConcept C33923547 @default.
• W4387642812 hasConcept C41008148 @default.
• W4387642812 hasConceptScore W4387642812C11413529 @default.
• W4387642812 hasConceptScore W4387642812C119857082 @default.
• W4387642812 hasConceptScore W4387642812C121332964 @default.
• W4387642812 hasConceptScore W4387642812C124101348 @default.
• W4387642812 hasConceptScore W4387642812C126255220 @default.
• W4387642812 hasConceptScore W4387642812C153294291 @default.
• W4387642812 hasConceptScore W4387642812C154945302 @default.
• W4387642812 hasConceptScore W4387642812C167928553 @default.
• W4387642812 hasConceptScore W4387642812C24552861 @default.
• W4387642812 hasConceptScore W4387642812C33923547 @default.
• W4387642812 hasConceptScore W4387642812C41008148 @default.
• W4387642812 hasLocation W43876428121 @default.
• W4387642812 hasOpenAccess W4387642812 @default.
• W4387642812 hasPrimaryLocation W43876428121 @default.
• W4387642812 hasRelatedWork W1666666856 @default.
• W4387642812 hasRelatedWork W2005054943 @default.
• W4387642812 hasRelatedWork W2068200129 @default.
• W4387642812 hasRelatedWork W2120758002 @default.
• W4387642812 hasRelatedWork W2276167504 @default.
• W4387642812 hasRelatedWork W2393565244 @default.
• W4387642812 hasRelatedWork W2654527859 @default.
• W4387642812 hasRelatedWork W2991924068 @default.
• W4387642812 hasRelatedWork W3188177659 @default.
• W4387642812 hasRelatedWork W583624554 @default.
• W4387642812 isParatext "false" @default.
• W4387642812 isRetracted "false" @default.
• W4387642812 workType "article" @default.