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Reduced-order models (ROMs) are computationally inexpensive simplificationsof high-fidelity complex ones. Such models can be found in computational fluiddynamics where they can be used to predict the characteristics of multiphaseflows. In previous work, we presented a ROM analysis framework that coupledcompression techniques, such as autoencoders (AE), with Gaussian process (GP)regression in the latent space. This pairing has significant advantages overthe standard encoding-decoding routine, such as the ability to interpolate orextrapolate in the initial conditions' space, which can provide predictionseven when simulation data are not available. In this work, we focus on thismajor advantage and show its effectiveness by performing the pipeline on threemultiphase flow applications. We also extend the methodology by using DeepGaussian Processes (DGP) as the interpolation algorithm and compare theperformance of our two variations, as well as another variation from theliterature that uses Long short-term memory (LSTM) networks, for theinterpolation.

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