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Reduced order model using convolutional auto-encoder with self-attention
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DOI：10.1063/5.0051155.png)
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Abstract
En 中文
In this paper, a novel reduced order model based on a convolutional auto-encoder with self-attention (SACAE ROM) is proposed. The proposed model is a non-intrusive reduced order model, which uses a convolutional neural network and a long short-term memory network to extract temporal feature relationships from high-fidelity numerical solutions. The self-attention is introduced into the convolutional neural network to enhance the non-local information perception ability of the convolutional neural network and improve the feature extraction ability of the network. The model adopts a joint construction method, which overcomes the problem of propagating error in each process of the model. The model proposed in this paper has been verified on the problem of the flow around a cylinder. The experimental results indicate that the SACAE ROM has higher robustness and accuracy. Compared with the ROM based on a convolutional auto-encoder, the prediction error of the SACAE ROM is reduced by 42.9%. As with other ROMs based on deep neural networks, the SACAE ROM takes a long time to train. To solve this problem, the transfer and generalization ability of the model is studied in this paper. In the experiment, the flow velocity and spoiler of the flow around the cylinder were changed, and the training time of the transfer model was reduced by about 50% to 60%. This result demonstrates that the problem of too long training time can be solved by transfer learning.
Keywords:
SYSTEM-IDENTIFICATION
NEURAL-NETWORKS
DECOMPOSITION
FLOWS
Journal
IF:
4.3
Papers:
2.9W
Citations:
8.0W