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Abstract: In this paper we describe a stochastic dynamic data-driven application system (DDDAS) for monitoring, in real-time, material damage in aerospace structures. The work involves experiments, different candidate damage models, finite element discretization, Bayesian analysis of the candidate models, Bayesian filtering with the most plausible model, parallel scientific libraries, and high performance computing. Here we describe a low-degree-of-freedom model designed for proof-of-concept, in preparation for the development of the full DDDAS. The physical system involves fiber-reinforced composite plates subjected to quasi-static loading and enriched with distributed carbon nanotubes that act as sensors, signaling damage through changes on the voltage profile. We give an overview of the experimental data we collected, of the damage models we explored, and of the Bayesian methodology we applied in order to use uncertain experimental data for driving the stochastic system. [Copyright &y& Elsevier]