When an unknown input needs to be estimated, one would typically use either a finite element model or a more data-driven modal model. Both types of models have their respective advantages, which we would like to exploit. Choosing only one model, however, leaves the information of the other type unused. An important observation is then that both types of models typically work best in different frequency ranges. In order to exploit both types, we therefore propose a weighted optimization method. For a certain frequency domain, the error with respect to a model is then weighted depending on the reliability of said model in that frequency domain. In order to use the proposed method in the time domain, we first take the Fourier transform of the time data. Then, the input reconstruction is performed in the frequency domain, and finally the inverse Fourier transform is taken of the result. This method was validated using an experimental beam setup. In this validation, the proposed method using two models outperformed the results of using either model separately.