Faculty Profile

(1) Reliability and Optimal Design of Complex Mechanical Structures

(2) Strength and Fatigue Life of Complex Mechanical Structures

(3) Digital Twin Technology and Its Applications

[39]张屹尚,张煜,杨旭锋*.融合自适应RBF模型和多模态优化重要抽样的小失效概率可靠性分析方法[J].电子科技大学学报,2025,54(06):840-849.

[38] Chen P, Chen L, Yang F, Yang X*. A new Kriging model for high-dimensional problems based on correlation analysis and adaptive stratification[J]. Structural and Multidisciplinary Optimization, 2025, 68: 265.

[37] Fan X, Yang X*, Liu Y*. System reliability analysis for rare events based on improved cross-entropy importance sampling and parallel learning strategy[J]. Applied Mathematical Modelling, 2025: 116583.

[36] Yang X*, Zhang Y, Chen P, Yang F. An active learning method for high-dimensional and small failure probability problems combining matrix-operation radial basis function model with matrix-operation hybrid optimization algorithm[J]. Computers & Structures, 2025, 319: 108007.

[35] Yang X*, Chen P, Chen L, Wei Y, Jiang W. Active learning for high-dimensional reliability using matrix-operation anisotropic RBF model embedded with Multiscale Graph Correlation[J]. Computer Methods in Applied Mechanics and Engineering, 2025, 446: 118286.

[34] Fan X, Zhang L, Yang X*, Zhang Z, Liu Y*. A single-loop active learning kriging method for failure probability upper bound function estimation[J]. Structures, 2025, 79: 109420.

[33] Fan X, Yang X*, Liu Y*. Two-stage failure probability function estimation method based on improved cross-entropy importance sampling and adaptive Kriging[J]. Reliability Engineering & System Safety, 2025: 111272.

[32] Yang X*, Jiang W, Zhang Y, et al. An active learning method combining MRBF model and dimension-reduction importance sampling for reliability analysis with high dimensionality and very small failure probability[J]. Reliability Engineering & System Safety, 2025, 261: 111107. https://doi.org/10.1016/j.ress.2025.111107.

[31] Tang W, Jiang W, Yang X*, Zheng Q*. Global sensitivity analysis of high-speed train dynamics system with high-dimensional inputs and multiple outputs[J]. Vehicle System Dynamics, 2024, https://doi.org/10.1080/00423114.2025.2453495.

[30] Fan X., Yang X*. and Liu Y*. A Novel Two-Stage Reliability Analysis Method Combining Improved Cross-Entropy Adaptive Sampling and Relevant Vector Machine. Int J Numer Methods Eng, 2024, https://doi.org/10.1002/nme.7635.

[29] Yang X*, Zhang Y, Zhao J, Jiang W. A novel active learning method based on matrix-operation RBF model for high-dimensional reliability analysis[J]. Computer Methods in Applied Mechanics and Engineering, 2024, 432: 117434.

[28] Yang X*, Zhang Y, Wang T, Zhang H*. An active learning reliability method combining population Monte Carlo and Kriging model for small failure probability[J]. Structures, 2024, 70: 107621.

[27] Tian W, Yang X*, Liu Y, Shi X, Fan X. Efficient damage prediction and sensitivity analysis in rectangular welded plates subjected to repeated blast loads utilizing deep learning networks[J]. Acta Mechanica, 2024.

[26] Fan X, Yang X*, Liu Y. A Kriging-assisted adaptive improved cross-entropy importance sampling method for random-interval hybrid reliability analysis[J]. Structural and Multidisciplinary Optimization, 2024, 67(9): 1-23.

[25] Wang T, Yang X*, Mi C. Error-guided method combining adaptive learning kriging model and parallel-tempering-based importance sampling for system reliability analysis[J]. Engineering Optimization, 2024, 56: 525-547.

[24] Yang X*, Cheng X, Liu Z, et al. An adaptive method fusing the kriging model and multimodal importance sampling for profust reliability analysis. Engineering Optimization, 2022, 54: 1870-1886.

[23] Yang X*, Cheng X, Liu Z, et al. A novel active learning method for profust reliability analysis based on the Kriging model. Engineering with Computers, 2022, 38: 3111-3124.

[22] Niu J, Lv D, Li R, Zhou D, Wang Y, Yang X*. Matching of multiple aerodynamic parameters for railway train/tunnel systems to ensure critical airtightness performance of high-speed trains. Structural and Multidisciplinary Optimization. 2022;66:4.

[21] Yang X*, Zeqing L, Cheng X. An enhanced active learning Kriging model for evidence theory-based reliability analysis. Structural and Multidisciplinary Optimization, 2021, 64: 2165-2181.

[20] Wang T, Yang X*, Mi C. An efficient hybrid reliability analysis method based on active learning Kriging model and multimodal-optimization-based importance sampling. International Journal for Numerical Methods in Engineering, 2021, 122: 7664-7682.

[19] Yang X*, Cheng X. Active learning method combining Kriging model and multimodal-optimization-based importance sampling for the estimation of small failure probability. International Journal for Numerical Methods in Engineering, 2020, 121: 4843-4864.

[18] Yang X*, Cheng X, Wang T, et al. System reliability analysis with small failure probability based on active learning Kriging model and multimodal adaptive importance sampling. Structural and Multidisciplinary Optimization, 2020, 62: 581-596.

[17]   Yang X*, Wang T, Li J, et al. Bounds approximation of limit-state surface based on active learning Kriging model with truncated candidate region for random-interval hybrid reliability analysis. International Journal for Numerical Methods in Engineering, 2020, 121: 1345-1366.

[16]   Yang X*, Mi C, Deng D, et al. A system reliability analysis method combining active learning Kriging model with adaptive size of candidate points. Structural and Multidisciplinary Optimization, 2019, 60: 137-150.

[15]   Yang X*, Liu Y, Mi C, et al. Active Learning Kriging Model Combining With Kernel-Density-Estimation-Based Importance Sampling Method for the Estimation of Low Failure Probability. Journal of Mechanical Design, 2018, 140: 051402.

[14]   Yang X*, Liu Y, Fang X, et al. Estimation of low failure probability based on active learning Kriging model with a concentric ring approaching strategy. Structural and Multidisciplinary Optimization, 2018, 58: 1175–1186.

[13]   Yang X*, Liu Y, Mi C, et al. System reliability analysis through active learning Kriging model with truncated candidate region. Reliability Engineering & System Safety, 2018, 169: 235-241.

[12]   Yang X*, Liu Y, Ma P. Structural reliability analysis under evidence theory using the active learning kriging model. Engineering Optimization, 2017, 49: 1922-1938.

[11]   Yang X, Liu Y, Gao Y. Unified reliability analysis by active learning Kriging model combining with Random‐set based Monte Carlo simulation method. International Journal for Numerical Methods in Engineering, 2016, 108: 1343-1361.

[10]   Yang X, Liu Y, Zhang Y, et al. Hybrid reliability analysis with both random and probability-box variables. Acta Mechanica, 2015, 226: 1341-1357.

[9]   Yang X, Liu Y, Zhang Y, et al. Probability and convex set hybrid reliability analysis based on active learning Kriging model. Applied Mathematical Modelling, 2015, 39: 3954-3971.

[8]   Yang X, Liu Y, Gao Y, et al. An active learning Kriging model for hybrid reliability analysis with both random and interval variables. Structural and Multidisciplinary Optimization, 2015, 51: 1003-1016.

[7]   Yang X, Liu Y, Zhang Y. Discussion of “Reliability‐based design optimization with dependent interval variables” by Xiaoping Du, International Journal for Numerical Methods in Engineering 2012; 91: 218–228[J]. International Journal for Numerical Methods in Engineering, 2014, 99(7): 542-544.

[6]   杨旭锋*,刘泽清,张懿.基于贝叶斯神经网络的金属材料P-S-N曲线估计[J].华南理工大学学报(自然科学版),2023,51(11):82-92.

[5]   姜杰,杨旭锋*,丁国富.基于多峰优化Kriging模型与距离相关系数的高速列车动力学参数多输出灵敏度分析[J].工程科学与技术,2024,56(04):250-260.DOI:10.15961/j.jsuese.202201078.

[4]   刘泽清,程鑫,杨旭锋*.基于ALK模型与子集模拟的主动学习可靠性分析方法[J].机械强度,2024,46(01):96-106.DOI:10.16579/j.issn.1001.9669.2024.01.013.

[3]   杨旭锋*,程鑫,刘泽清.一种融合交叉熵自适应抽样与ALK模型的可靠性分析方法[J].机械工程学报,2024,60(16):73-82.

[2]   陈哲,杨旭锋*,程鑫.基于改进Kriging模型的主动学习可靠性分析方法[J].机械强度,2021,43(01):129-136.DOI:10.16579/j.issn.1001.9669.2021.01.019.

[1]   杨旭锋*,刘永寿,何勇.飞机起落架收放机构功能可靠性与灵敏度分析[J].航空制造技术,2014,(03):78-81+85.DOI:10.16080/j.issn1671-833x.2014.03.017.

[1] General Program of the National Natural Science Foundation of China: Research on Reliability Analysis Methods for Complex Mechanical Structures Based on Active Learning Radial Basis Function Models in High Dimensions,Jan. 2025 – Dec. 2028, ongoing, Principal Investigator.

[2] Young Scientists Fund of the National Natural Science Foundation of China: Research on Reliability Analysis Methods Based on Active Learning Kriging Models for Small Failure Probabilities, Jan. 2018 – Dec. 2020, completed, Principal Investigator.

[3] Key R&D Project of the Science and Technology Department of Sichuan Province: Research on Key Technologies for Reliability Analysis and Optimal Design of Aerospace Composite Structures Based on Active Learning Kriging Models, Apr. 2021 – Mar. 2024, completed, Principal Investigator.

[4] Commissioned Project from Enterprises/Institutions: Development and Debugging of Flight Trajectory Simulation System and Task Allocation System, completed, Principal Investigator. 

[5] Commissioned Project from Enterprises/Institutions: Strength, Buckling Analysis, and Optimization of Composite Structure Shells, completed, Principal Investigator.

[6] Commissioned Project from Enterprises/Institutions: Reliability Analysis of Vibration Fatigue Life of Bogie Frames Based on Subset Simulation Methods, completed, Principal Investigator.

[7] Commissioned Project from Enterprises/Institutions: Impact Energy Release Testing of Active Materials, completed, Principal Investigator.