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A Multilevel Domain Decomposition Approach for Studying Coupled Flow Applications

Communications in Computational Physics 2009年第7期6卷 319-341页

作者： E.Aulisa a.cervone S.Manservisi P.Seshaiyer Department of Mathematics and Statistics Texas Tech UniversityLubbockTX 79409-1042USA Laboratory of Montecuccolino DIENCAUniversity of BolognaBologna40136Italy Department of Mathematical Sciences George Mason UniversityFairfaxVA 22030USA

In this paper,a multilevel domain decomposition approach based on multigrid methods for obtaining fast solutions for coupled engineering flow applications arising on complex domains is *** proposed technique not only allows solutions to be computed efficiently at the element level but also helps us to achieve proper accuracy,load balancing and computational *** results presented demonstrate the robustness of the proposed technique.

关键词： Finiteelementmethods multigridmethods domaindecomposition

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Spatiotemporal event detection: a review

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International Journal of Digital Earth 2020年第12期13卷 1339-1365页

作者： Manzhu Yu Myra Bambacus Guido cervone Keith Clarke Daniel Duffy Qunying Huang Jing Li Wenwen Li Zhenlong Li Qian Liu Bernd Resch Jingchao Yang Chaowei Yang Department of Geography Pennsylvania State UniversityUniversity ParkPAUSA NASA Goddard Space Flight Center NASA Goddard Space Flight CenterGreenbeltMDUSA Department of Geography University of California-Santa BarbaraSanta BarbaraCAUSA Department of Geography University of Wisconsin-MadisonMadisonWIUSA Department of Geography and the Environment University of DenverDenverCOUSA School of Geographical Sciences and Urban Planning Arizona State UniversityTempeAZUSA Department of Geography University of South CarolinaColumbiaSCUSA NSF Spatiotemporal Innovation Center&Dept.of Geography and GeoInformation Science George Mason UniversityFairfaxVAUSA Department of Geoinformatics University of SalzburgKapitelgasseSalzburgAustria Center for Geographic Analysis Harvard UniversityBostonMAUSA

The advancements of sensing technologies,including remote sensing,in situ sensing,social sensing,and health sensing,have tremendously improved our capability to observe and record natural and social phenomena,such as natural disasters,presidential elections,and infectious *** observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment,urban settings,health and disease propagation,business decisions,and crisis and *** event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant *** paper reviews the literature for different sensing capabilities,spatiotemporal event extraction methods,and categories of applications for the detected *** novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow(from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events)as an agenda for future event detection *** is presented on the current challenges to this research agenda,and future directions are discussed for conducting spatiotemporal event detection in the era of big data,advanced sensing,and artificial intelligence.

关键词： GeoAI geography and geoscience human dynamics digital earth computational challenges cloud computing internet of things

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