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SUMMARY:A Triple Model Reduction for Data-Driven Large-Scale Inverse Probl
 ems in High Dimensional Parameter Spaces - Tan Bui-Thanh (University of Te
 xas at Austin)
DTSTART:20180305T144500Z
DTEND:20180305T153000Z
UID:TALK101809@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:<span>Co-authors: Ellen Le		(The University of Texas At Austin
 )\, Aaron Myers		(The University of Texas At Austin)\, Brad Marvin		(The U
 niversity of Texas At Austin)\, Vishwas Rao		(Argone National Lab)        
 <br></span><span><br>We present an approach to address the challenge of da
 ta-driven large-scale inverse problems in high dimensional parameter space
 s. The idea is to combine a goal-oriented model reduction approach for sta
 te\, data-informed/active-subspace reduction for parameter\, and randomize
 d misfit approach for data reduction. The method is designed to mitigate t
 he bottle neck of large-scale PDE solve\, of high dimensional parameter sp
 ace exploration\, and of ever-increasing volume of data. Various theoretic
 al and numerical results will be presented to support the proposed approac
 h.&nbsp\;</span>
LOCATION:Seminar Room 1\, Newton Institute
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