Optimal sensor placement for the quantification of model uncertainty: A functional analysis perspective

We consider optimal sensor placement for inverse problems constrained by partial differential equations in which the model contains uncertainties, both in the form of a model bias and uncertainties in the model parameters. We demonstrate the link between the sensors and the stability of the inverse problem through an observability coefficient that quantifies the influence of the sensor choice on the accuracy. We present a framework for the greedy selection of sensor locations that sequentially improves this observability coefficient and thus reduces the uncertainty. We then demonstrate the links to standard optimality criteria in Bayesian optimal experimental design.

***Due to the current pandemic this colloquium will be conducted online. We invite you to join and spread the news. We will send out an invitation for a zoom meeting via our email list. If you are not on the mail list already, please send an email up front to liv.heinecke[at]uni-potsdam.de ***