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A major challenge in next-generation industrial applications is to improvenumerical analysis by quantifying uncertainties in predictions. In this work wepresent a formulation of a fully nonlinear and dispersive potential flow waterwave model with random inputs for the probabilistic description of theevolution of waves. The model is analyzed using random sampling techniques andnon-intrusive methods based on generalized Polynomial Chaos (PC). These methodsallow to accurately and efficiently estimate the probability distribution ofthe solution and require only the computation of the solution in differentpoints in the parameter space, allowing for the reuse of existing simulationsoftware. The choice of the applied methods is driven by the number ofuncertain input parameters and by the fact that finding the solution of theconsidered model is computationally intensive. We revisit experimentalbenchmarks often used for validation of deterministic water wave models. Basedon numerical experiments and assumed uncertainties in boundary data, ouranalysis reveals that some of the known discrepancies from deterministicsimulation in comparison with experimental measurements could be partiallyexplained by the variability in the model input. We finally present a syntheticexperiment studying the variance based sensitivity of the wave load on anoff-shore structure to a number of input uncertainties. In the numericalexamples presented the PC methods have exhibited fast convergence, suggestingthat the problem is amenable to being analyzed with such methods.

Efficient uncertainty quantification of a fully nonlinear and dispersive water wave model with random inputs