Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator

• URL: http://arxiv.org/abs/2304.06972v1
• Date: Fri, 14 Apr 2023 07:46:03 GMT
• Title: Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator
• Authors: Yanfang Lyu, Xiaoyu Zhao, Zhiqiang Gong, Xiao Kang and Wen Yao
• Abstract summary: This work proposes a novel multi-fidelity learning method based on the Fourier Neural Operator. It uses abundant low-fidelity data and limited high-fidelity data under transfer learning paradigm. Three typical fluid and temperature prediction problems are chosen to validate the accuracy of the proposed multi-fidelity model.
• Score: 10.104417481736833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-driven prediction of fluid flow and temperature distribution in marine and aerospace engineering has received extensive research and demonstrated its potential in real-time prediction recently. However, usually large amounts of high-fidelity data are required to describe and accurately predict the complex physical information, while in reality, only limited high-fidelity data is available due to the high experiment/computational cost. Therefore, this work proposes a novel multi-fidelity learning method based on the Fourier Neural Operator by jointing abundant low-fidelity data and limited high-fidelity data under transfer learning paradigm. First, as a resolution-invariant operator, the Fourier Neural Operator is first and gainfully applied to integrate multi-fidelity data directly, which can utilize the scarce high-fidelity data and abundant low-fidelity data simultaneously. Then, the transfer learning framework is developed for the current task by extracting the rich low-fidelity data knowledge to assist high-fidelity modeling training, to further improve data-driven prediction accuracy. Finally, three typical fluid and temperature prediction problems are chosen to validate the accuracy of the proposed multi-fidelity model. The results demonstrate that our proposed method has high effectiveness when compared with other high-fidelity models, and has the high modeling accuracy of 99% for all the selected physical field problems. Significantly, the proposed multi-fidelity learning method has the potential of a simple structure with high precision, which can provide a reference for the construction of the subsequent model.

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