姓 名：林泉
职 称：讲师
邮 箱：quanlin@hust.edu.cn
个人简介
林泉，华中科技大学航空航天学院讲师。于2024年3月获华中科技大学机械工程专业博士学位，2024年4月至2026年4月在华中科技大学航空航天学院从事博士后研究，出站后留校从事教学和科研工作。
主要从事飞行器智能设计方法、飞行器数字孪生技术方向研究。近年来，以第一/通讯作者在AIAA Journal、Journal of Mechanical Design、Knowledge-Based Systems等国内外期刊发表学术论文10余篇。主持国家自然科学基金青年项目、中国博士后科学基金会与湖北省联合资助（特别资助）项目、中国博士后科学基金面上项目、上海市经济信息化委工业互联网专项基金等科研项目。
研究方向
飞行器智能设计方法、飞行器数字孪生技术
科研项目
[1] 国家自然科学基金青年科学基金项目，52405269，主持
[2] 中国博士后科学基金会与湖北省联合资助（特别资助）项目，2025T018HB，主持
[3] 中国博士后科学基金面上项目，2024M761000，主持
[4] 上海市经济信息化委工业互联网专项基金，060300006520250092，主持
代表性论文
[1] Lin, Q., Hu, J., Zhou, Q., et al. (2021). Multi-output Gaussian process prediction for computationally expensive problems with multiple levels of fidelity. Knowledge-Based Systems, 227, 107151.
[2] Lin, Q., Hu, J., Zhou, Q., et al. (2024). A multi-fidelity Bayesian optimization approach for constrained multi-objective optimization problems. Journal of Mechanical Design, 146(7), 071702.
[3] Lin, Q., Hu, J., Zhang, L., et al. (2022). Gradient-enhanced multi-output Gaussian process model for simulation-based engineering design. AIAA Journal, 60(1), 76-91.
[4] Lin, Q., Hu, D., Hu, J., et al. (2021). A screening-based gradient-enhanced Gaussian process regression model for multi-fidelity data fusion. Advanced Engineering Informatics, 50, 101437.
[5] Lin, Q., Zhou, Q., Hu, J., et al. (2022). A sequential sampling approach for multi-fidelity surrogate modeling-based robust design optimization. Journal of Mechanical Design, 144(11), 111703.
[6] Lin, Q., Gong, L., Zhang, Y., et al. (2022). A probability of improvement-based multi-fidelity robust optimization approach for aerospace products design. Aerospace Science and Technology, 128, 107764.
[7] Lin, Q., Qian, J., Cheng, Y., et al. (2022). A multi-output multi-fidelity Gaussian process model for non-hierarchical low-fidelity data fusion. Knowledge-Based Systems, 254, 109645.
[8] Lin, Q., Zheng, A., Hu, J., et al. (2023). A multi-objective Bayesian optimization approach based on variable-fidelity multi-output metamodeling. Structural and Multidisciplinary Optimization, 66, 100.
[9] Luo S., Qian J., Geng Y., Zhou Q., Lin Q.* (2025). Toward Efficient Digital Twin Simulation: A Causal Representation Learning Approach. Knowledge-Based Systems, 114442.
[10] Kou, M., Dong, X., Liu, H., Zhou, Q., Lin, Q.* (2026). An adaptive multi-fidelity surrogate-based robust optimization approach considering the combined effect of various uncertainties. Engineering with Computers, 42(2), 53.
