Scalable Methods for Optimal Experimental Design and Optimal Control for Systems Governed by PDEs wi

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  We formulate an A-optimal experimental design criterion for infinitedimensional nonlinear Bayesian inverse problems.Our method aims to minimize the average variance of a Gaussian approximation to posterior law of inversion parameters by solving a bi-level PDE-constrained optimization problem.I will also discuss risk-averse optimization under uncertainty with application to optimal control of PDEs with uncertain parameter fields.We use numerical results for a porous medium flow problem with uncertain permeability to illustrate the methods.
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