杨旭锋

副教授

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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论文成果

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[1]     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.

[2]     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.

[3]     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.

[4]     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.

[5]     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.

[6]     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.

[7]     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.

[8]     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.

[9]     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.

[10]   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.

[11]   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.

[12]   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.

[13]   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.

[14]   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.

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

[16]   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.

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

[18]   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.

[19]   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.

[20]   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.

[21]   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.

[22]   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.

[23]   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.

[24]   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].

[25]   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.

[26]   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.


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