Reduced basis methods for optimal experimental design of parametrized linear evolution problems

Abstract

In this paper we propose an algorithm for the bi-level optimal experimental design involving a parameter-dependent evolution problems. In the inner cycle a control is fixed and the parameter is optimized in order to minimize a cost function that measure the discrepancy from some data. In the outer cycle the found parameter is fixed and the control is now optimized in order to minimize a suitable measure of uncertainty of the parameters. The inner cycle uses a trust-region reduced basis approximation of the model with creation and enrichment of the reduced basis on-the-fly. Numerical examples illustrate the efficiency of the proposed approach.