This paper presents a methodology [1] devoted to updating the control parameters of large computational models in linear structural dynamics. In particular, we are interested in the linear vibrations in the frequency domain of complex structures that are characterized by the presence of numerous structural elastic modes, in particular local modes. These local modes arise from the multilevel nature of the structure and are spread throughout the entire frequency band. In such a context, it is assumed that one call to the computational model entails a very high computational cost, such that no more than a hundred calls can reasonably be considered. Furthermore, it is assumed that only one target response is available for identifying the control parameters. The optimization problem is not convex and since only a small number of realizations is affordable, it is proposed to use the Probabilistic Learning on Manifold (PLoM) [2] to generate additional realizations. Then, the cost function is evaluated using conditional statistics. The additional realizations are learned based on the few (less than a hundred) realizations that constitute the training set. The PloM method is particularly tailored to the case of small data and consequently, it is well indicated in this case. As a numerical application, the proposed methodology using PLoM and conditional statistics .is validated for the case of a simple structure that features high modal density. [1] O. Ezvan, C. Soize, C. Desceliers, R. Ghanem, Updating an uncertain and expensive computational model in structural dynamics based on one single target FRF using a probabilistic learning tool, Computational Mechanics, doi: 10.1007/s00466-023-02301-2, 71, 1161-1177 (2023). [2] C. Soize, R. Ghanem, Probabilistic learning on manifolds (PLoM) with partition, International Journal for Numerical Methods in Engineering, doi: 10.1002/nme.6856, 123(1), 268-290 (2022).