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Seminar: Numerical Analysis and Scientific Computing
D-optimal Experimental Design for Bayesian Inverse Problems
Dr. Arvind Krishna Saibaba, NC State University
Contact: Lars Ruthotto,
Venue: Mathematics and Science Center, Room W301
Optimal Experimental Design seeks to control experimental conditions in order to maximize the amount of information gained about parameters of interest, subject to physical or budgetary constraints. The parameters we wish to infer are represented on fine-scale grids; consequently, the experimental design problem is extremely computationally challenging and efficient algorithms are needed. We develop a computational framework for the D-optimality criterion in PDE based inverse problems. Our approach exploits a certain low-rank structure in the covariance matrices using novel randomized estimators. This approach allows us to reduce the computational costs by several orders of magnitude compared to naive approaches. We demonstrate our algorithms on an optimal sensor placement problem from contaminant source identification.\\ \\Joint work with Alen Alexanderian, Ilse CF Ipsen (both at Department of Mathematics, NCSU)