An improved sufficient dimension reduction-based Kriging modeling method for high-dimensional evaluation-expensive problems-Reference-Cited by-同舟云学术

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An improved sufficient dimension reduction-based Kriging modeling method for high-dimensional evaluation-expensive problems

Published:2024-01 Issue: Volume:418 Page:116544
ISSN:0045-7825
Container-title:Computer Methods in Applied Mechanics and Engineering
language:en
Short-container-title:Computer Methods in Applied Mechanics and Engineering

Author:

Song Zhouzhou,Liu Zhao,Zhang Hanyu,Zhu Ping

Funder

National Natural Science Foundation of China

China Scholarship Council

Shanghai Jiao Tong University

Natural Science Foundation of Shanghai Municipality

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Reference60 articles.

1. A sequential calibration and validation framework for model uncertainty quantification and reduction;Jiang;Comput. Methods Appl. Mech. Eng.,2020

2. A collaborative model calibration framework under uncertainty considering parameter distribution;Feng;Comput. Methods Appl. Mech. Eng.,2023

3. Data-driven and active learning of variance-based sensitivity indices with Bayesian probabilistic integration;Song;Mech. Syst. Signal Process.,2022

4. Uncertainty quantification in multiscale simulation of woven fiber composites;Bostanabad;Comput. Methods Appl. Mech. Eng.,2018

5. A stochastic process discretization method combing active learning Kriging model for efficient time-variant reliability analysis;Zhang;Comput. Methods Appl. Mech. Eng.,2021

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