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Towards Dynamic Data-Driven Management of the Ruby Gulch Waste Repository
[chapter]
2006
Lecture Notes in Computer Science
Previous work in the Instrumented Oil-Field DDDAS project has enabled a new generation of data-driven, interactive and dynamically adaptive strategies for subsurface characterization and oil reservoir management. This work has led to the implementation of advanced multiphysics, multi-scale, and multi-block numerical models and an autonomic software stack for DDDAS applications. The stack implements a Gridbased adaptive execution engine, distributed data management services for real-time data
doi:10.1007/11758532_52
fatcat:62infqcvj5eyfpa6coy7i7kti4