A data-driven metamodel-based approach for point force localization

This paper introduces a novel strategy for point force localization in the frequency domain, based on metamodeling techniques and independent of the excitation level. More precisely, the ability of well-established techniques, such as Polynomial Chaos expansion or Universal Kriging, in providing accurate surrogate models for locating a point force through an optimization procedure is evaluated. The proposed methodology is applied in a purely data-driven context. Obtained results highlight the good performance of the proposed strategy for relatively small data sets, as well as its robustness to noise in both training and deployment phases.