  Reduced-order models (ROMs) are computationally inexpensive simplifications
of high-fidelity complex ones. Such models can be found in computational fluid
dynamics where they can be used to predict the characteristics of multiphase
flows. In previous work, we presented a ROM analysis framework that coupled
compression techniques, such as autoencoders (AE), with Gaussian process (GP)
regression in the latent space. This pairing has significant advantages over
the standard encoding-decoding routine, such as the ability to interpolate or
extrapolate in the initial conditions' space, which can provide predictions
even when simulation data are not available. In this work, we focus on this
major advantage and show its effectiveness by performing the pipeline on three
multiphase flow applications. We also extend the methodology by using Deep
Gaussian Processes (DGP) as the interpolation algorithm and compare the
performance of our two variations, as well as another variation from the
literature that uses Long short-term memory (LSTM) networks, for the
interpolation.

Computational Physics;Fluid Dynamics