Two-fidelity Data Fusion Modeling with Particle Swarm Sampling
1. School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China
2. Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang 621050, China
| Abstract: | Computer simulation and experimental testing are commonly used means of data acquisition in industrial design, so the combination of low-precision data represented by cheap calculation data and high-precision data represented by expensive test data for fusion modeling is a hot topic in practical applications question. In order to improve the quality of the model and control the usage of high-precision data, this paper proposes a double-precision fusion modeling method based on particle swarm sampling. The algorithm uses the Kriging model to model high- and low-precision data, and uses PSO (Particle swarm optimization) to obtain low-precision model information for high-precision modeling. Combined with the characteristics of sufficient amount of low-precision data, high-precision data is expensive and difficult to obtain, the low-precision model modeled after sufficient sampling is used to guide the initial sampling of the high-precision model, and the fusion model is obtained by training with two high-precision and low-precision models, and two The point-adding criterion is used for sequential modeling, and the fusion model is continuously optimized. The algorithm is tested on a standard function and an application problem. The experimental results show that the algorithm improves the quality of the modeling without increasing the amount of high-precision data, which verifies the effectiveness of the proposed algorithm. |
| Keywords: | Two-fidelity Data; Fusion Modeling; Particle Swarm Optimization; Sequential Design of Experiments; Kriging Model |
| DOI: | 10.57237/j.wjms.2023.01.002 |
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Received: 27 March 2023
Accepted: 17 May 2023
Published Online: 25 May 2023
Funding
| 1. | 国家自然科学基金项目 (12171385) |
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