Bayesian source identification using local priors. (January 2016) - Aucejo, M.; De Smet, O.
Abstract: This paper is concerned with the development of a general methodology for identifying mechanical sources from prior local information on both their nature and location over the studied structure. For this purpose, the formulation of the identification problem is derived from the Bayesian statistics, that provides a flexible way to account for local a priori on the distribution of sources. Practically, the resulting optimization problem can be seen as a group generalized Tikhonov regularization, that is solved in an iterative manner. The main features of the proposed identification method are illustrated with both numerical and experimental examples. In particular, it is shown that properly exploiting the local spatial information drastically improves the quality of the source identification. Abstract : Highlights: A Bayesian formulation of the identification problem using local priors is presented. The formulation is highly flexible to describe prior knowledge of the sources. The method is validated both numerically and experimentally. Exploiting local information drastically improves the quality of the identification.