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This project focuses on the development of new computational methods,
particularly using parallel and grid computing, to address environmental
problems that require interactions between multiple types of computational
models. These models include physical ones, hydrology and weather being
examples, and biological ones, which may be for particular species
populations or for communities of interacting populations. These models may
be operating at different spatial and temporal resolutions, and may utilize
quite different mathematical approaches, such as matrix models, differential
equation models and individual-based models. An overall objective is to
develop new techniques that allow for efficient solution of linked models
for which parallel methods are appropriate. An additional objective is to
provide a framework for potential use of these parallel methods by resource
managers to compare and contrast the effects of alternative management of
natural systems, and eventually lead to the capability to carry out optimal
dynamic
and spatial control of natural systems. This project has been supported through funding from the National
Science Foundation to the University of Tennessee through Awards
DEB-0219269 and IIS-0427471.
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