A multi-fidelity transfer learning strategy based on multi-channel fusion
文献类型：期刊论文
| 作者 | Zhang, Zihan3; Ye, Qian1; Yang, Dejin1,4; Wang, Na2 ; Meng, Guoxiang3
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| 刊名 | JOURNAL OF COMPUTATIONAL PHYSICS
 |
| 出版日期 | 2024-06-01 |
| 卷号 | 506页码:112952 |
| 关键词 | Surrogate model Deep neural networks Multi-fidelity Data fusion |
| ISSN号 | 0021-9991 |
| DOI | 10.1016/j.jcp.2024.112952 |
| 产权排序 | 4 |
| 英文摘要 | Multi -fidelity strategies leverage a large amount of low -fidelity data combined with a smaller set of high-fidelity data, thereby achieving satisfactory results at a reasonable cost. In our research, we introduce an innovative multi -fidelity strategy that integrates the concepts of multi -fidelity data fusion and transfer learning. In the proposed framework, we incorporate auto -encoders and a multi -channel transfer learning strategy, enabling the network model to comprehend the relationship between the low -fidelity and high-fidelity models in both explicit and implicit manners. This approach not only enhances prediction accuracy but also mitigates issues such as overfitting and negative transfer, which may arise in scenarios with sparse samples. Additionally, Bayesian optimization is employed for effective hyperparameter selection. To evaluate and analyze the performance of our proposed method, we present a series of benchmark test cases. Furthermore, we also show the application of the proposed method to engineering problems. Firstly, we consider a parametrized partial differential equation problem, where high-fidelity and low -fidelity data are obtained using exact methods and simplified algorithms, respectively. Subsequently, we extend this strategy to convolutional neural network architectures, specifically addressing a pressure Poisson equation problem. We also explore the effect of the reliability of the low -fidelity data and the number of high-fidelity data on the results. The results show that the proposed method exhibits low requirements in terms of both the reliability of the low -fidelity data and the number of high-fidelity data while maintaining satisfactory accuracy metrics. |
| WOS关键词 | OPTIMIZATION ; SURFACES |
| 资助项目 | National Key Basic Research and Development Program of China[2021YFC2203501] ; National Natural Science Foundation of China[U1931137] |
| WOS研究方向 | Computer Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001221359200001 |
| 出版者 | ACADEMIC PRESS INC ELSEVIER SCIENCE |
| 资助机构 | National Key Basic Research and Development Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.xao.ac.cn/handle/45760611-7/6619]  |
| 专题 | 脉冲星研究团组 |
| 通讯作者 | Ye, Qian |
| 作者单位 | 1.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China 2.Chinese Acad Sci, Xinjiang Observ, Xinjiang 830011, Peoples R China 3.Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China 4.Chongqing Jiaotong Univ, Sch Civil Engn, Chongqing 400074, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Zihan,Ye, Qian,Yang, Dejin,et al. A multi-fidelity transfer learning strategy based on multi-channel fusion[J]. JOURNAL OF COMPUTATIONAL PHYSICS,2024,506:112952. |
| APA | Zhang, Zihan,Ye, Qian,Yang, Dejin,Wang, Na,&Meng, Guoxiang.(2024).A multi-fidelity transfer learning strategy based on multi-channel fusion.JOURNAL OF COMPUTATIONAL PHYSICS,506,112952. |
| MLA | Zhang, Zihan,et al."A multi-fidelity transfer learning strategy based on multi-channel fusion".JOURNAL OF COMPUTATIONAL PHYSICS 506(2024):112952. |
入库方式： OAI收割
来源：新疆天文台
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