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- W1659282449 abstract "Traditional simulation-based applications for exploring a parameter space to understand a physical phenomenon or to optimize a design are rapidly overwhelmed by data volume when large numbers of simulations of different parameters are carried out. Optimizing reservoir management through simulation-based studies, in which large numbers of realizations are sought using detailed geologic descriptions, is an example of such applications. In this paper, we describe a software architecture to facilitate large scale simulation studies, involving ensembles of long-running simulations and analysis of vast volumes of output data. This architecture is built on top of two frameworks we have developed: IPARS and DataCutter. These frameworks make it possible to implement tools and applications to run large-scale simulatios, and generate and investigate terabyte-scale datasets efficiently." @default.
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- W1659282449 date "2003-01-01" @default.
- W1659282449 modified "2023-10-17" @default.
- W1659282449 title "Driving Scientific Applications by Data in Distributed Environments" @default.
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- W1659282449 doi "https://doi.org/10.1007/3-540-44864-0_37" @default.
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