  Bayesian calibration is widely used for inverse analysis and uncertainty
analysis for complex systems in the presence of both computer models and
observation data. In the present work, we focus on large-scale fluid-structure
interaction systems characterized by large structural deformations. Numerical
methods to solve these problems, including embedded/immersed boundary methods,
are typically not differentiable and lack smoothness. We propose a framework
that is built on unscented Kalman filter/inversion to efficiently calibrate and
provide uncertainty estimations of such complicated models with noisy
observation data. The approach is derivative-free and non-intrusive, and is of
particular value for the forward model that is computationally expensive and
provided as a black box which is impractical to differentiate. The framework is
demonstrated and validated by successfully calibrating the model parameters of
a piston problem and identifying the damage field of an airfoil under transonic
buffeting.
