In this paper, we consider a new framework where two types of data are available:experimental dataY1,...,Ynsupposed to be i.i.d from Y and outputs from a simulated reduced model.We develop a procedure for parameter estimation to characterize a feature of thephenomenon Y. We prove a risk bound qualifying the proposed procedure interms of the number of experimental data n, reduced model complexity andcomputing budget m. The method we present is general enough to cover awide range of applications. To illustrate our procedure we provide a numericalexample.
