  Fiber metal laminates (FML) are composite structures consisting of metals and
fiber reinforced plastics (FRP) which have experienced an increasing interest
as the choice of materials in aerospace and automobile industries. Due to a
sophisticated built up of the material, not only the design and production of
such structures is challenging but also its damage detection. This research
work focuses on damage identification in FML with guided ultrasonic waves (GUW)
through an inverse approach based on the Bayesian paradigm. As the Bayesian
inference approach involves multiple queries of the underlying system, a
parameterized reduced-order model (ROM) is used to closely approximate the
solution with considerably less computational cost. The signals measured by the
embedded sensors and the ROM forecasts are employed for the localization and
characterization of damage in FML. In this paper, a Markov Chain Monte-Carlo
(MCMC) based Metropolis-Hastings (MH) algorithm and an Ensemble Kalman
filtering (EnKF) technique are deployed to identify the damage. Numerical tests
illustrate the approaches and the results are compared in regard to accuracy
and efficiency. It is found that both methods are successful in multivariate
characterization of the damage with a high accuracy and were also able to
quantify their associated uncertainties. The EnKF distinguishes itself with the
MCMC-MH algorithm in the matter of computational efficiency. In this
application of identifying the damage, the EnKF is approximately thrice faster
than the MCMC-MH.
