Machine Learning for Fluid Mechanics

05/27/2019
by   Steven Brunton, et al.
0

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning presents us with a wealth of techniques to extract information from data that can 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. We outline fundamental machine learning methodologies and discuss 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 links data with modeling, experiments, and simulations. Machine learning provides a powerful information processing framework that can augment, and possibly even transform, current lines of fluid mechanics research and industrial applications.

READ FULL TEXT
research
03/28/2023

The transformative potential of machine learning for experiments in fluid mechanics

The field of machine learning has rapidly advanced the state of the art ...
research
10/05/2021

Applying Machine Learning to Study Fluid Mechanics

This paper provides a short overview of how to use machine learning to b...
research
08/06/2018

The Fluid Mechanics of Liquid Democracy

Liquid democracy is the principle of making collective decisions by lett...
research
09/20/2023

Swarm Mechanics and Swarm Chemistry: A Transdisciplinary Approach for Robot Swarms

This paper for the first time attempts to bridge the knowledge between c...
research
09/17/2021

What machine learning can do for computational solid mechanics

Machine learning has found its way into almost every area of science and...
research
06/27/2023

CrunchGPT: A chatGPT assisted framework for scientific machine learning

Scientific Machine Learning (SciML) has advanced recently across many di...
research
05/27/2021

Flow based features and validation metric for machine learning reconstruction of PIV data

Reconstruction of flow field from real sparse data by a physics-oriented...

Please sign up or login with your details

Forgot password? Click here to reset