Compressing fluid flows with nonlinear machine learning: mode decomposition, latent modeling, and flow control

TL;DR

This paper reviews the use of convolutional neural network autoencoders for compressing fluid flow data, highlighting their applications in mode decomposition, latent modeling, and flow control to enhance fluid dynamics understanding.

Contribution

It provides a comprehensive overview of CNN-AE structures, principles, and applications in fluid mechanics, emphasizing the role of nonlinear machine learning in flow data compression.

Findings

CNN-AE effectively compresses high-dimensional flow data.

Nonlinear compression aids in flow mode analysis and control.

The approach bridges data-driven modeling with fluid mechanics insights.

Abstract

An autoencoder is a self-supervised machine-learning network trained to output a quantity identical to the input. Owing to its structure possessing a bottleneck with a lower dimension, an autoencoder works to achieve data compression, extracting the essence of the high-dimensional data into the resulting latent space. We review the fundamentals of flow field compression using convolutional neural network-based autoencoder (CNN-AE) and its applications to various fluid dynamics problems. We cover the structure and the working principle of CNN-AE with an example of unsteady flows while examining the theoretical similarities between linear and nonlinear compression techniques. Representative applications of CNN-AE to various flow problems, such as mode decomposition, latent modeling, and flow control, are discussed. Throughout the present review, we show how the outcomes from the nonlinear machine-learning-based compression may support modeling and understanding a range of fluid mechanics problems.