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Accession Number ADA578547
Title Uncertainity Quantification for Large Scale Inverse Scattering.
Publication Date Apr 2013
Media Count 8p
Personal Author G. Biros
Abstract Our goal is the design of fast parallel algorithms statistical inference for scalar and wave propagation problems. We have looked at source inversion and inverse medium problem problems. We use a Bayesian approach in which the regularization appears as prior information and the data mismatch appears as a likelihood information, given known noise probability density functions. A key component of all of our algorithms is the approximation of the Hessian operator. Key components of our work are rank-revealing factorizations, fast extraction of the diagonal of the inverse, adaptivity, and integration of all of these components within a particle filter methodology. In addition, our implementations are being designed to scale on manycore and heterogeneous parallel architectures.
Keywords Algorithms
Approximation(Mathematics)
Bayes theorem
Electromagnetic scattering
Fast multipole methods
Hessian operators
Inverse scattering
Uncertainty
Uncertainty quantification


 
Source Agency Non Paid ADAS
NTIS Subject Category 72B - Algebra, Analysis, Geometry, & Mathematical Logic
72F - Statistical Analysis
46H - Radiofrequency Waves
Corporate Author Texas Univ. at Austin. Inst. for Computational Engineering and Sciences.
Document Type Technical report
Title Note Final rept. 1 Sep 2009-1 Oct 2012.
NTIS Issue Number 1321
Contract Number FA9550-09-1-0679

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