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This paper presents a methodology to efficiently design food supply chains.
We develop a probabilistic graphical model based methodology to efficiently perform uncertainty quantification in the presence of both stochastic input and multiple scales.
We provide the scaling analyses performed on the Stampede supercomputer and numerical examples that illustrate the capability of our methodology to efficiently simulate different aspects of epitaxial growth.
In general, the results demonstrate the ability of this design methodology to efficiently estimate burner performance and evaluate design modifications to accommodate varying fuel compositions.
Production of recombinant proteins is the starting point for biochemical and biophysical analyses and requires methodology to efficiently proceed from gene sequence to purified protein.
DDDAS is a driver for LSBC and LSBD environments and but also a methodology to efficiently manage and exploitthese large-scale-heterogeneous resources, aspects which will be addressed in the talk; additional examples of DDDAS-based new capabilities for such applications are provided in other papers in this workshop.
Similar(46)
Available approaches to the investigation of traumatic brain injury (TBI) are frequently hampered, to some extent, by the unsatisfactory abilities of existing methodologies to efficiently define and represent affected structural connectivity and functional mechanisms underlying TBI-related pathology.
They must also include methodologies to efficiently compare different solutions/scenarios.
These are the key features of information that require particular tools and methodologies to efficiently address them.
Development of novel methodologies to efficiently create large genetic epidemiology cohorts is needed.
Here, we present a methodology, Eris, to efficiently and accurately compute the stability changes of proteins upon mutations using our protein-modeling suite, Medusa.
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