A practical guide to machine learning in fluid mechanics: an encoder–decoder perspective

This paper discusses how machine learning could support studying fluid mechanics. We first offer the so-called encoder–decoder perspective that can accommodate a range of problem settings fluid mechanicians often tackle with data-driven techniques. Focusing on nonlinear machine-learning analyses, representative efforts on regression, data compression, prediction and system control are introduced, following the given picture of data-driven fluid mechanics. We then provide a step-by-step guideline for studying fluid flows in a data-driven manner – (i) setting the problem up, (ii) curating the data, (iii) constructing a model, (iv) training a model, (v) assessing the machine-learned outcomes, and (vi) improving the techniques – while discussing what we fluid mechanicians should be mindful of. Importantly, each step necessitates carefully incorporating prior knowledge of flow physics to appropriately design machine-learning studies for complex fluid flows, rather than naively applying black-box techniques. We further perform a case study on data-driven compression of a turbulent wake around an aerofoil, offering example approaches for investigating three-dimensional, unsteady turbulent flows. We hope that this perspective will serve as a practical guide for designing machine-learning studies in fluid mechanics.