# Machine Learning for Fluid Mechanics

## TL;DR

This paper reviews how machine learning techniques are transforming fluid mechanics research by enabling better data analysis, modeling, and control of fluid flows across various scales and applications.

## Contribution

It provides a comprehensive overview of machine learning methodologies applied to fluid mechanics, highlighting current developments, challenges, and future opportunities.

## Key findings

- Machine learning enhances understanding and modeling of complex fluid flows.
- ML algorithms automate flow control and optimization tasks.
- The approach integrates data-driven methods into traditional fluid mechanics research.

## Abstract

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.