杨旭锋

副教授

博士生导师 硕士生导师

学历:博士研究生毕业

学位:工学博士学位

办公地点:西南交通大学机械馆2414

在职信息:在岗

毕业院校:西北工业大学

所在单位:机械工程学院

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科学研究

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论文成果

    [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/OL].机械工程学报,1-10[2024-09-21].

    [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.


研究领域

    (1)复杂机械结构可靠性与优化设计

    (2)复杂机械结构强度与疲劳寿命

    (3)数字孪生技术及其应用

专利

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著作成果

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