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Award Abstract #0218142
ITR: DDDAS Generalized Polynomial Chaos: Parallel Algorithms for Modeling and Propagating Uncertainty in Physical and Biological Systems
| NSF Org: |
CNS
Division of Computer and Network Systems
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| Initial Amendment Date: |
September 5, 2002 |
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| Latest Amendment Date: |
August 9, 2004 |
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| Award Number: |
0218142 |
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| Award Instrument: |
Continuing grant |
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| Program Manager: |
Frederica Darema
CNS Division of Computer and Network Systems
CSE Directorate for Computer & Information Science & Engineering
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| Start Date: |
September 1, 2002 |
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| Expires: |
August 31, 2006 (Estimated) |
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| Awarded Amount to Date: |
$422040 |
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| Investigator(s): |
George Karniadakis gk@cfm.brown.edu (Principal Investigator)
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| Sponsor: |
Brown University
BOX 1929
Providence, RI 02912 401/863-2777
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| NSF Program(s): |
ITR SMALL GRANTS
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| Field Application(s): |
0000099 Other Applications NEC
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| Program Reference Code(s): |
HPCC, 9218, 1656,
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| Program Element Code(s): |
1686
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ABSTRACT
EIA-0218142
George E. Karniadakis
Brown University
ITR/DDDAS Generalized Polynomial Chaos: Parallel Algorithms for Modeling and Propagating Uncertainty in Physical and Biological Systems
The applications we target are prototype problems in bioengineering and in nanotechnology. The coupled nature of such problems and the many parameters involved provide a good testbed for evaluating the performance of the new algorithms at resolutions from 0.1 to 1 billion degrees-of-freedom. The sources of uncertainty may be caused by incomplete knowledge or fluctuations in boundary or initial conditions, geometric domain, transport coefficients, mechanical properties, and other external forcing or volumetric sources.
The proposed work will have significant and broad impact as it will establish a composite error bar in large-scale simulations and will enable numerical stochastic approaches to large-scale simulations of physical and biological systems. It will also benefit many other fields including climate and network/web traffic modeling, where current uncertainty modeling approaches are inadequate. Stochastically simulated responses can serve as sensitivity analysis that could potentially guide experimental work and dynamic instrumentation.
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