Compressing fluid flows with nonlinear machine learning: mode decomposition, latent modeling, and flow control
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911030163013632 |
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| author | Fukagata, Koji Fukami, Kai |
| author_facet | Fukagata, Koji Fukami, Kai |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_00343 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Compressing fluid flows with nonlinear machine learning: mode decomposition, latent modeling, and flow control Fukagata, Koji Fukami, Kai Fluid Dynamics 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. |
| title | Compressing fluid flows with nonlinear machine learning: mode decomposition, latent modeling, and flow control |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2505.00343 |