Generalized polynomial chaos based surrogate models for acoustics and vibrations

This work explores generalized polynomial chaos (GPC) surrogate models and their effectiveness for general acoustics and vibration applications. GPC is primarily known as an uncertainty quantification (UQ) technique and in that context the underlying polynomial-based model has been shown to be effective in mapping input probability distributions to the corresponding output probability distributions. In this study, GPC surrogate models – including those generated by both quadrature and regression methods – are evaluated for their effectiveness in non-UQ focused analyses. As points of comparison, Krylov subspace and other more traditional reduced order modeling techniques are demonstrated and compared to GPC models so that the differences may be better understood. Example problems with several different levels of complexity are used to show how the computational burden as well as the overall effectiveness of the method changes as the number of input variables that must be considered grows.