Research on Gaussian process surrogate model and its application to rocket aerodynamic analysis

被引：0
|
作者：
Liu, Xin-Liang [1 ,2 ]
Zhang, Kun-Lun [2 ]
Guo, Bo [2 ]
机构：
[1] Department of Rear Service, Logistics Command Academy, Beijing 100858, China
[2] College of Information Systems and Management, National Univ. of Defense Technology, Changsha 410073, China
来源：
Guti Huojian Jishu/Journal of Solid Rocket Technology | 2010年 / 33卷 / 05期
关键词：
D O I：
暂无
中图分类号：
学科分类号：
摘要：
19
引用
收藏
页码：486 / 490
相关论文
共 50 条
  • [21] Gaussian Process Regression and Its Application in Near-Infrared Spectroscopy Analysis
    Feng Ai-ming
    Fang Li-min
    Lin Min
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31 (06) : 1514 - 1517
  • [22] Design and Analysis for the Gaussian Process Model
    Jones, Bradley
    Johnson, Rachel T.
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2009, 25 (05) : 515 - 524
  • [23] Model selection with application to gamma process and inverse Gaussian process
    Zhang, M.
    Revie, M.
    RISK, RELIABILITY AND SAFETY: INNOVATING THEORY AND PRACTICE, 2017, : 1505 - 1511
  • [24] An enhanced optimization approach based on Gaussian process surrogate model for process control in injection molding
    Xia, Wei
    Luo, Bin
    Liao, Xiao-ping
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2011, 56 (9-12): : 929 - 942
  • [25] An enhanced optimization approach based on Gaussian process surrogate model for process control in injection molding
    Wei Xia
    Bin Luo
    Xiao-ping Liao
    The International Journal of Advanced Manufacturing Technology, 2011, 56 : 929 - 942
  • [26] A Gaussian-Process-Based Data-Driven Traffic Flow Model and Its Application in Road Capacity Analysis
    Liu, Zhiyuan
    Lyu, Cheng
    Wang, Zelin
    Wang, Shuaian
    Liu, Pan
    Meng, Qiang
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2023, 24 (02) : 1544 - 1563
  • [27] Transfer learning Gaussian process regression surrogate model with explainability for structural reliability analysis under variation in uncertainties
    Saida, Taisei
    Nishio, Mayuko
    COMPUTERS & STRUCTURES, 2023, 281
  • [28] AMFGP: An active learning reliability analysis method based on multi-fidelity Gaussian process surrogate model
    Lu, Ning
    Li, Yan-Feng
    Mi, Jinhua
    Huang, Hong-Zhong
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2024, 246
  • [29] Risk-informed sensitivity analysis and optimization of seismic mitigation strategy using Gaussian process surrogate model
    Zhong, Jian
    Wan, Hua-Ping
    Yuan, Wancheng
    He, Min
    Ren, Wei-Xin
    SOIL DYNAMICS AND EARTHQUAKE ENGINEERING, 2020, 138
  • [30] System fragility analysis of highway bridge using multi-output Gaussian process regression surrogate model
    Saida, Taisei
    Rashid, Muhammad
    Nishio, Mayuko
    ADVANCES IN STRUCTURAL ENGINEERING, 2024, 27 (16) : 2803 - 2822
12345