ABSTRACT
An information theoretic framework for optimal experimental design is presented. The objective function is rooted in information theory, and is the expected Kullback-Leibler divergence between the prior and posterior pdf in a Bayesian framework. In this way we seek designs which will yield data that are most informative for model parameter inference. In general, the objective function has to be estimated by a Monte Carlo sum, which means that its evaluation requires a large number of model runs. Asymptotic approximations are introduced to significantly reduce these runs. The optimization of the objective function is performed using stochastic optimization methods such as CMA-ES to avoid premature convergence to local optimal usually manifested in optimal experimental design problems. The framework is demonstrated using applications from mechanics. Two optimal sensor placement problems are solved: 1) parameter estimation in non-linear model of simply supported beam under uncertain load, 2) modal identification.
