# Multiphase flow applications of nonintrusive reduced-order models with Gaussian process emulation

@article{Botsas2022MultiphaseFA, title={Multiphase flow applications of nonintrusive reduced-order models with Gaussian process emulation}, author={Themistoklis Botsas and Indranil Pan and Lachlan Robert Mason and Omar K. Matar}, journal={Data-Centric Engineering}, year={2022}, volume={3} }

Abstract 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, with Gaussian process regression in the latent space. This pairing has significant advantages over the standard encoding–decoding…

## One Citation

### An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes

- Computer Science, EngineeringPhysics of Fluids
- 2022

This paper presents a new AI-based non-intrusive reduced-order model within a domain decomposition framework (AI-DDNIROM), which is capable of making predictions for domains significantly larger than the domain used in training.

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