Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system

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Turbulence Modeling in the Age of Data (2018, methodology): “Ma et al. (2015, 2016) used machine learning to model the inter-phase mass and momentum ﬂuxes in multiphase ﬂow simulations.”
Enhancing computational fluid dynamics with machine learning (2021, methodology): “[72] used deep learning to predict the closure terms (i.”
Quantification of model uncertainty in RANS simulations: A review (2018, methodology): “For example, in multiphase flow simulations, models are used to describe interphase mass and momentum exchanges in averaged equations [175,176]; in climate and weather modeling, parameterization are used to account for unresolved or unknown physics including radiation, cloud, and boundary layer proc”
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework (2017, methodology): “Data-driven closure modeling beyond RANS simulations In addition to RANS modeling as reviewed above, data and machine learning have been used to provide closures for (1) the subgrid-scale (SGS) fluxes for in LES, (2) the inter-phase momentum fluxes in multiphase flow simulations [25, 26], and (3) th”