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Article summary:
1. Reduced-order models (ROMs) are used to approximate higher-order dynamical systems with lower-order models while maintaining reasonable accuracy.
2. Deep learning (DL) has been proven to perform effectively in feature recognition and dimensionality reduction, such as convolutional neural networks (CNN) and autoencoder (AE).
3. A hierarchical autoencoder and temporal convolutional neural network reduced-order model is proposed for the turbulent wake of a three-dimensional bluff body, which can be used to optimize flow control laws and predict the temporal dynamics of unsteady flows more quickly.
Article analysis:
The article provides an overview of the use of deep learning for reduced order modeling in fluid dynamics, specifically focusing on the application of a hierarchical autoencoder and temporal convolutional neural network reduced-order model for the turbulent wake of a three-dimensional bluff body. The article is well written and provides a comprehensive overview of the topic, including relevant background information on linear theory based ROMs, deep learning techniques, and previous research efforts in this area.
The article is generally reliable and trustworthy; however, there are some potential biases that should be noted. For example, the authors focus primarily on their own research efforts rather than providing an unbiased overview of other approaches or methods that could be used for reduced order modeling in fluid dynamics. Additionally, there is no discussion or exploration of possible risks associated with using deep learning techniques for this purpose. Furthermore, there is no mention of any counterarguments or alternative points of view regarding the use of deep learning for reduced order modeling in fluid dynamics.
In conclusion, this article provides a comprehensive overview of deep learning techniques for reduced order modeling in fluid dynamics; however, it does not provide an unbiased overview or explore potential risks associated with its use.