On closures for reduced order models $-$ A spectrum of first-principle to machine-learned avenues

TL;DR

This paper reviews the evolution of reduced order models in fluid mechanics, emphasizing how data-driven approaches, including machine learning, have transformed ROM closures and modeling strategies over the past two decades.

Contribution

It highlights the impact of data-driven modeling on ROM closures, illustrating principles and discussing future directions for integrating machine learning into ROM development.

Findings

Data-driven modeling significantly improves ROM closures.

Modern machine learning techniques enhance the accuracy of under-resolved simulations.

The paper outlines future prospects for data-driven ROM methodologies.

Abstract

For over a century, reduced order models (ROMs) have been a fundamental discipline of theoretical fluid mechanics. Early examples include Galerkin models inspired by the Orr-Sommerfeld stability equation and numerous vortex models, of which the von K\'arm\'an vortex street is one of the most prominent. Subsequent ROMs typically relied on first principles, like mathematical Galerkin models, weakly nonlinear stability theory, and two- and three-dimensional vortex models. Aubry et al. [N. Aubry, P. Holmes, J. Lumley, and E. Stone, Journal of Fluid Mechanics, 192, 115-173 (1988)] pioneered data-driven proper orthogonal decomposition (POD) modeling. In early POD modeling, available data was used to build an optimal basis, which was then utilized in a classical Galerkin procedure to construct the ROM. But data has made a profound impact on ROMs beyond the Galerkin expansion. In this paper, we take a modest step and illustrate the impact of data-driven modeling on one significant ROM area. Specifically, we focus on ROM closures, which are correction terms that are added to classical ROMs in order to model the effect of the discarded ROM modes in under-resolved simulations. Through simple examples, we illustrate the main modeling principles used to construct classical ROMs, motivate and introduce modern ROM closures, and show how data-driven modeling, artificial intelligence, and machine learning have changed the standard ROM methodology over the last two decades. Finally, we outline our vision on how state-of-the-art data-driven modeling can continue to reshape the field of reduced order modeling.