Haiyun Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Haiyun Liu has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Aibo Xu
Wuhan Fiberhome Technical Services Co., Ltd
1 shared publication - Cong Zhang
School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China
1 shared publication - Jiacheng Liu
School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China
1 shared publication - Jiwei Hu
China–Chile ICT Belt and Road Joint Laboratory, Wuhan 430205, China
1 shared publication - Jiyin Cao
School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China
1 shared publication
Latest Publications
- Open Access
- 26 January 2024
- Published by MDPI AG in Mathematics
- Vol. 12 (3) , 396
Derivative-Variance Hybrid Global Sensitivity Measure with Optimal Sampling Method Selection
This paper proposes a derivative-variance hybrid global sensitivity measure with optimal sampling method selection. The proposed sensitivity measure is as computationally efficient as the derivative-based global sensitivity measure, which also serves as the conservative estimation of the corresponding variance-based global sensitivity measure. Moreover, the optimal sampling method for the proposed sensitivity measure is studied. In search of the optimal sampling method, we investigated the performances of six widely used sampling methods, namely Monte Carlo sampling, Latin hypercube sampling, stratified sampling, Latinized stratified sampling, and quasi-Monte Carlo sampling using the Sobol and Halton sequences. In addition, the proposed sensitivity measure is validated through its application to a rural bridge.
- 26 January 2024
- Published by MDPI AG in Mathematics
- Vol. 12 (3) , 396
Open AccessDerivative-Variance Hybrid Global Sensitivity Measure with Optimal Sampling Method Selection
This paper proposes a derivative-variance hybrid global sensitivity measure with optimal sampling method selection. The proposed sensitivity measure is as computationally efficient as the derivative-based global sensitivity measure, which also serves as the conservative estimation of the corresponding variance-based global sensitivity measure. Moreover, the optimal sampling method for the proposed sensitivity measure is studied. In search of the optimal sampling method, we investigated the performances of six widely used sampling methods, namely Monte Carlo sampling, Latin hypercube sampling, stratified sampling, Latinized stratified sampling, and quasi-Monte Carlo sampling using the Sobol and Halton sequences. In addition, the proposed sensitivity measure is validated through its application to a rural bridge.