FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2018248045> ?p ?o ?g. }

• W2018248045 endingPage "139" @default.
• W2018248045 startingPage "127" @default.
• W2018248045 abstract "The majority of literature regarding optimized Latin hypercube sampling (OLHS) is devoted to increasing the efficiency of these sampling strategies through the development of new algorithms based on the combination of innovative space-filling criteria and specialized optimization schemes. However, little attention has been given to the impact of the initial design that is fed into the optimization algorithm, on the efficiency of OLHS strategies. Previous studies, as well as codes developed for OLHS, have relied on one of the following two approaches for the selection of the initial design in OLHS: (1) the use of random points in the hypercube intervals (random LHS), and (2) the use of midpoints in the hypercube intervals (midpoint LHS). Both approaches have been extensively used, but no attempt has been previously made to compare the efficiency and robustness of their resulting sample designs. In this study we compare the two approaches and show that the space-filling characteristics of OLHS designs are sensitive to the initial design that is fed into the optimization algorithm. It is also illustrated that the space-filling characteristics of OLHS designs based on midpoint LHS are significantly better those based on random LHS. The two approaches are compared by incorporating their resulting sample designs in Monte Carlo simulation (MCS) for uncertainty propagation analysis, and then, by employing the sample designs in the selection of the training set for constructing non-intrusive polynomial chaos expansion (NIPCE) meta-models which subsequently replace the original full model in MCSs. The analysis is based on two case studies involving numerical simulation of density dependent flow and solute transport in porous media within the context of seawater intrusion in coastal aquifers. We show that the use of midpoint LHS as the initial design increases the efficiency and robustness of the resulting MCSs and NIPCE meta-models. The study also illustrates that this relative improvement decreases with increasing number of sample points and input parameter dimensions. Since the computational time and efforts for generating the sample designs in the two approaches are identical, the use of midpoint LHS as the initial design in OLHS is thus recommended." @default.
• W2018248045 created "2016-06-24" @default.
• W2018248045 creator A5003031201 @default.
• W2018248045 creator A5013075319 @default.
• W2018248045 creator A5035628177 @default.
• W2018248045 date "2015-02-01" @default.
• W2018248045 modified "2023-09-27" @default.
• W2018248045 title "Efficiency enhancement of optimized Latin hypercube sampling strategies: Application to Monte Carlo uncertainty analysis and meta-modeling" @default.
• W2018248045 cites W123341604 @default.
• W2018248045 cites W1528483814 @default.
• W2018248045 cites W1548602103 @default.
• W2018248045 cites W1604224450 @default.
• W2018248045 cites W1874589985 @default.
• W2018248045 cites W1940059766 @default.
• W2018248045 cites W1948985936 @default.
• W2018248045 cites W1964971655 @default.
• W2018248045 cites W1965847964 @default.
• W2018248045 cites W1970273374 @default.
• W2018248045 cites W1973584191 @default.
• W2018248045 cites W1979823707 @default.
• W2018248045 cites W1981270896 @default.
• W2018248045 cites W1991079954 @default.
• W2018248045 cites W1992395327 @default.
• W2018248045 cites W1994665973 @default.
• W2018248045 cites W2003553868 @default.
• W2018248045 cites W2010053796 @default.
• W2018248045 cites W2014910287 @default.
• W2018248045 cites W2024060531 @default.
• W2018248045 cites W2033753530 @default.
• W2018248045 cites W2037639866 @default.
• W2018248045 cites W2045355467 @default.
• W2018248045 cites W2049774453 @default.
• W2018248045 cites W2056145269 @default.
• W2018248045 cites W2070425636 @default.
• W2018248045 cites W2073687248 @default.
• W2018248045 cites W2076113041 @default.
• W2018248045 cites W2077145642 @default.
• W2018248045 cites W2079001474 @default.
• W2018248045 cites W2083166992 @default.
• W2018248045 cites W2094476403 @default.
• W2018248045 cites W2095319309 @default.
• W2018248045 cites W2097927798 @default.
• W2018248045 cites W2111815505 @default.
• W2018248045 cites W2113337191 @default.
• W2018248045 cites W2114154970 @default.
• W2018248045 cites W2122507884 @default.
• W2018248045 cites W2125572194 @default.
• W2018248045 cites W2136602340 @default.
• W2018248045 cites W2143591652 @default.
• W2018248045 cites W2145475762 @default.
• W2018248045 cites W2152710595 @default.
• W2018248045 cites W2154137997 @default.
• W2018248045 cites W2155726536 @default.
• W2018248045 cites W2160231391 @default.
• W2018248045 cites W2167720109 @default.
• W2018248045 cites W2169092059 @default.
• W2018248045 cites W2171993497 @default.
• W2018248045 cites W2256578114 @default.
• W2018248045 cites W2284436039 @default.
• W2018248045 cites W2315411895 @default.
• W2018248045 cites W2318802957 @default.
• W2018248045 cites W2568283272 @default.
• W2018248045 cites W2749771969 @default.
• W2018248045 cites W4247680473 @default.
• W2018248045 doi "https://doi.org/10.1016/j.advwatres.2014.12.008" @default.
• W2018248045 hasPublicationYear "2015" @default.
• W2018248045 type Work @default.
• W2018248045 sameAs 2018248045 @default.
• W2018248045 citedByCount "61" @default.
• W2018248045 countsByYear W20182480452016 @default.
• W2018248045 countsByYear W20182480452017 @default.
• W2018248045 countsByYear W20182480452018 @default.
• W2018248045 countsByYear W20182480452019 @default.
• W2018248045 countsByYear W20182480452020 @default.
• W2018248045 countsByYear W20182480452021 @default.
• W2018248045 countsByYear W20182480452022 @default.
• W2018248045 countsByYear W20182480452023 @default.
• W2018248045 crossrefType "journal-article" @default.
• W2018248045 hasAuthorship W2018248045A5003031201 @default.
• W2018248045 hasAuthorship W2018248045A5013075319 @default.
• W2018248045 hasAuthorship W2018248045A5035628177 @default.
• W2018248045 hasBestOaLocation W20182480452 @default.
• W2018248045 hasConcept C104317684 @default.
• W2018248045 hasConcept C105795698 @default.
• W2018248045 hasConcept C106131492 @default.
• W2018248045 hasConcept C11413529 @default.
• W2018248045 hasConcept C126255220 @default.
• W2018248045 hasConcept C129848803 @default.
• W2018248045 hasConcept C140779682 @default.
• W2018248045 hasConcept C173608175 @default.
• W2018248045 hasConcept C177803969 @default.
• W2018248045 hasConcept C185592680 @default.
• W2018248045 hasConcept C19499675 @default.
• W2018248045 hasConcept C197656079 @default.
• W2018248045 hasConcept C20820323 @default.
• W2018248045 hasConcept C2524010 @default.
• W2018248045 hasConcept C31972630 @default.
• W2018248045 hasConcept C33923547 @default.

• next