Multiphase flow applications of non-intrusive reduced-order models with Gaussian process emulation

Reduced-order models are computationally inexpensive simplifications of high-fidelity complex models. Such models can be found in computational fluid dynamics, e.g. in multiphase flow applications. We present a reduced-order model analysis framework where we couple compression techniques such as autoencoders with Gaussian process regression, and its natural extension: the Deep Gaussian Process (DGP) in the latent space. The mixture has some significant advantages as opposed to the standard encoding-decoding routine, primarily, the ability to interpolate or extrapolate in the initial conditions, which can offer results even when simulations are unavailable. We compare the methodology with alternative interpolation algorithms (long short-term memories) using exemplars from multiphase flow applications, and we examine how each variation affects our analysis.