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

Title: 利用非线性机器学习压缩流体流动：模式分解、潜在建模与流场控制

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. Show Chinese abstract

Abstract: 自编码器是一种自监督的机器学习网络，其训练目标是输出与输入完全相同的量。由于其结构具有较低维度的瓶颈层，自编码器的工作原理是对数据进行压缩，将高维数据的本质提取到结果的潜在空间中。本文回顾了基于卷积神经网络的自编码器（CNN-AE）在流场压缩中的基本原理及其在各类流体力学问题中的应用。我们通过一个非定常流动的例子来介绍CNN-AE的结构和工作原理，并探讨线性和非线性压缩技术之间的理论相似性。文中还讨论了CNN-AE在模态分解、潜在建模和流动控制等典型流体问题中的应用。在整个综述过程中，我们展示了基于非线性机器学习的压缩方法如何支持建模和理解一系列流体力学问题。