On the Cover: Contour Method with Uncertainty Quantification: A Robust and Optimised Framework via Gaussian Process Regression

被引：0
|
作者：
机构：
来源：
Experimental Mechanics | 2022年 / 62卷 / 8期
关键词：
D O I：
10.1007/s11340-022-00899-7
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：1243 / 1243
相关论文
共 50 条
  • [1] Contour Method with Uncertainty Quantification: A Robust and Optimised Framework via Gaussian Process Regression
    A. Tognan
    L. Laurenti
    E. Salvati
    [J]. Experimental Mechanics, 2022, 62 : 1305 - 1317
  • [2] Contour Method with Uncertainty Quantification: A Robust and Optimised Framework via Gaussian Process Regression
    Tognan, A.
    Laurenti, L.
    Salvati, E.
    [J]. EXPERIMENTAL MECHANICS, 2022, 62 (08) : 1305 - 1317
  • [3] A hybrid data-driven-physics-constrained Gaussian process regression framework with deep kernel for uncertainty quantification
    Chang, Cheng
    Zeng, Tieyong
    [J]. JOURNAL OF COMPUTATIONAL PHYSICS, 2023, 486
  • [4] A Gaussian process robust regression
    Murata, N
    Kuroda, Y
    [J]. PROGRESS OF THEORETICAL PHYSICS SUPPLEMENT, 2005, (157): : 280 - 283
  • [5] A Gaussian Process Method with Uncertainty Quantification for Air Quality Monitoring
    Wang, Peng
    Mihaylova, Lyudmila
    Chakraborty, Rohit
    Munir, Said
    Mayfield, Martin
    Alam, Khan
    Khokhar, Muhammad Fahim
    Zheng, Zhengkai
    Jiang, Chengxi
    Fang, Hui
    [J]. ATMOSPHERE, 2021, 12 (10)
  • [6] Simultaneous quantification of epistemic and aleatory uncertainty in GMPEs using Gaussian process regression
    Marcel Hermkes
    Nicolas M. Kuehn
    Carsten Riggelsen
    [J]. Bulletin of Earthquake Engineering, 2014, 12 : 449 - 466
  • [7] Simultaneous quantification of epistemic and aleatory uncertainty in GMPEs using Gaussian process regression
    Hermkes, Marcel
    Kuehn, Nicolas M.
    Riggelsen, Carsten
    [J]. BULLETIN OF EARTHQUAKE ENGINEERING, 2014, 12 (01) : 449 - 466
  • [8] Multi-output local Gaussian process regression: Applications to uncertainty quantification
    Bilionis, Ilias
    Zabaras, Nicholas
    [J]. JOURNAL OF COMPUTATIONAL PHYSICS, 2012, 231 (17) : 5718 - 5746
  • [9] Robust Gaussian Process Regression With Input Uncertainty: A PAC-Bayes Perspective
    Liu, Tianyu
    Lu, Jie
    Yan, Zheng
    Zhang, Guangquan
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2024, 54 (02) : 962 - 973
  • [10] Distributed robust Gaussian Process regression
    Sebastian Mair
    Ulf Brefeld
    [J]. Knowledge and Information Systems, 2018, 55 : 415 - 435
12345