庞勇，工学博士，大连理工大学在职博士后（合作导师：郭东明院士），长期从事代理模型优化、不确定性分析与可靠性优化、数字孪生、工业软件开发等方面的研究工作。博士毕业于大连理工大学（导师：宋学官教授）。

在机械、力学、计算机等领域期刊上发表论文40余篇，其中以第一作者发表SCI论文11篇，参与撰写学术著作一部，申请/授权国家发明专利7项。博士期间连续3次获国家奖学金，获大连理工大学优秀研究生标兵称号，获复杂装备可靠性工程与科学研讨会研究生论坛一等奖等论文/竞赛奖励10余项。申请人作为研究骨干参与国家重点研发计划项目等多项国家级课题。

教育经历

(1) 2021-09 至 2025-03, 大连理工大学, 机械设计及理论, 博士

(2) 2019-09 至 2021-06, 大连理工大学, 机械设计及理论, 其他

(3) 2015-09 至 2019-06, 大连理工大学, 机械设计制造及其自动化, 学士

工作经历

(1) 2025-03 至 今, 大连理工大学, 机械工程学院, 助理研究员

学术论文

[1]  Pang Y, Shi M, Zhang L, et al. PR-FCM: a polynomial regression-based fuzzy C-means algorithm for attribute-associated data[J]. Information Sciences, 2022, 585: 209-231. 

[2]  Pang Y, Wang Y, Zhang S, et al. An expensive many-objective optimization algorithm based on efficient expected hypervolume improvement[J]. IEEE Transactions on Evolutionary Computation, 2022, 27(6): 1822-1836. 

[3]  Pang Y, Yang L, Wang Y, et al. A Latin hypervolume design for irregular sampling spaces and its application in the analysis of cracks[J]. Engineering with Computers, 2023, 39(5): 3509-3526.

[4]  Pang Y, Lai X, Zhang S, et al. A Kriging-assisted global reliability-based design optimization algorithm with a reliability-constrained expected improvement[J]. Applied Mathematical Modelling, 2023, 121: 611-630. 

[5]  Pang Y, Wang Y, Lai X, et al. Enhanced Kriging leave-one-out cross-validation in improving model estimation and optimization[J]. Computer Methods in Applied Mechanics and Engineering, 2023, 414: 116194. 

[6]  Pang Y, Hu Z, Zhang S, et al. Co-design of an unmanned cable shovel for structural and control integrated optimization: A highly heterogeneous constrained multi-objective optimization algorithm[J]. Applied Energy, 2024, 376: 124325.

[7]  Pang Y, Zhang S, Liang P, et al. Surrogate model uncertainty quantification for active learning reliability analysis[J]. Chinese Journal of Aeronautics, 2024.

[8]  Pang Y, Shi M, Zhang L, et al. A multivariate time series segmentation algorithm for analyzing the operating statuses of tunnel boring machines[J]. Knowledge-Based Systems, 2022, 242: 108362.

[9]     Pang Y, Wang Y, Sun W, et al. OTL-PEM: an optimization-based two-layer pointwise ensemble of surrogate models[J]. Journal of Mechanical Design, 2022, 144(5): 051702. 

[10]   Pang Y, Zhang S, Jin Y, et al. Surrogate information transfer and fusion in high-dimensional expensive optimization problems[J]. Swarm and Evolutionary Computation, 2024, 88: 101586.

[11]   Pang Y, Lai X, Wang Y, et al. Surrogate-assisted expensive constrained bi-objective optimization with highly heterogeneous evaluations[J]. Swarm and Evolutionary Computation, 2023, 83: 101401.

[12]   Lai X, Pang Y, Liu F, et al. A multi-fidelity surrogate model based on design variable correlations[J]. Advanced Engineering Informatics, 2024, 59: 102248.

[13]  Wang Y, Pang Y, Xue T, et al. Ensemble learning based hierarchical surrogate model for multi-fidelity information fusion[J]. Advanced Engineering Informatics, 2024, 60: 102535. 

[14]  Zhang S, Pang Y, Li Q, et al. Multi-type data fusion via transfer learning surrogate modeling and its engineering application[J]. Information Sciences, 2024: 120918. 

[15]  Lai X, Pang Y, Zhang S, et al. An adaptive ensemble of surrogate models based on heuristic model screening[J]. Structural and Multidisciplinary Optimization, 2022, 65(12): 351. 

[16]  Zhang S, Pang Y, Liang P, et al. On the ensemble of surrogate models by minimum screening index[J]. Journal of Mechanical Design, 2022, 144(7): 071707.

[17]  Yang L, Pang Y, He X, et al. An active learning-driven optimal sensor placement method considering sensor position distribution toward structural health monitoring[J]. Structural and Multidisciplinary Optimization, 2024, 67(12): 1-23. 

[18]  Zhang S, Pang Y, Liu F, et al. Random projection enhancement: A Novel method for improving performance of surrogate models[J]. Swarm and Evolutionary Computation, 2024, 89: 101645.

[19]  Wang Y, Pang Y, Sun W, et al. Industrial data denoising via low-rank and sparse representations and its application in tunnel boring machine[J]. Energies, 2022, 15(10): 3525.