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

通讯/办公地址:

邮箱:

论文成果

当前位置: 中文主页 >> 科学研究 >> 论文成果

[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.


报考该导师研究生的方式

欢迎你报考杨旭锋老师的研究生,报考有以下方式:

1、参加西南交通大学暑期夏令营活动,提交导师意向时,选择杨旭锋老师,你的所有申请信息将发送给杨旭锋老师,老师看到后将和你取得联系,点击此处参加夏令营活动

2、如果你能获得所在学校的推免生资格,欢迎通过推免方式申请杨旭锋老师研究生,可以通过系统的推免生预报名系统提交申请,并选择意向导师为杨旭锋老师,老师看到信息后将和你取得联系,点击此处推免生预报名

3、参加全国硕士研究生统一招生考试报考杨旭锋老师招收的专业和方向,进入复试后提交导师意向时选择杨旭锋老师。

4、如果你有兴趣攻读杨旭锋老师博士研究生,可以通过申请考核或者统一招考等方式报考该导师博士研究生。

点击关闭