Physics-agnostic and Physics-infused machine learning for thin films flows: modeling, and predictions from small data
Abstract
Numerical simulations of multiphase flows are crucial in numerous engineering applications, but are often limited by the computationally demanding solution of the Navier-Stokes (NS) equations. Here, we present a data-driven workflow where a handful of detailed NS simulation data are leveraged into a reduced-order model for a prototypical vertically falling liquid film. We develop a physics-agnostic model for the film thickness, achieving a far better agreement with the NS solutions than the asymptotic Kuramoto-Sivashinsky (KS) equation. We also develop two variants of physics-infused models providing a form of calibration of a low-fidelity model (i.e. the KS) against a few high-fidelity NS data. Finally, predictive models for missing data are developed, for either the amplitude, or the full-field velocity and even the flow parameter from partial information. This is achieved with the so-called "Gappy Diffusion Maps", which we compare favorably to its linear counterpart, Gappy POD.
Cite
@article{arxiv.2301.12508,
title = {Physics-agnostic and Physics-infused machine learning for thin films flows: modeling, and predictions from small data},
author = {Cristina P. Martin-Linares and Yorgos M. Psarellis and Georgios Karapetsas and Eleni D. Koronaki and Ioannis G. Kevrekidis},
journal= {arXiv preprint arXiv:2301.12508},
year = {2023}
}