  Structural health monitoring (SHM) systems use the non-destructive testing
principle for damage identification. As part of SHM, the propagation of
ultrasonic guided waves (UGWs) is tracked and analyzed for the changes in the
associated wave pattern. These changes help identify the location of a
structural damage, if any. We advance existing research by accounting for
uncertainty in the material and geometric properties of a structure. The
physics model used in this study comprises of a monolithically coupled system
of acoustic and elastic wave equations, known as the wave propagation in
fluid-solid and their interface (WpFSI) problem. As the UGWs propagate in the
solid, fluid, and their interface, the wave signal displacement measurements
are contrasted against the benchmark pattern. For the numerical solution, we
develop an efficient algorithm that successfully addresses the inherent
complexity of solving the multiphysics problem under uncertainty. We present a
procedure that uses Gaussian process regression and convolutional neural
network for predicting the UGW propagation in a solid-fluid and their interface
under uncertainty. First, a set of training images for different realizations
of the uncertain parameters of the inclusion inside the structure is generated
using a monolithically-coupled system of acoustic and elastic wave equations.
Next, Gaussian processes trained with these images are used for predicting the
propagated wave with convolutional neural networks for further enhancement to
produce high-quality images of the wave patterns for new realizations of the
uncertainty. The results indicate that the proposed approach provides an
accurate prediction for the WpFSI problem in the presence of uncertainty.
