Multi-output Gaussian process prediction for computationally expensive problems with multiple levels of fidelity
出版年份 2021 全文链接
标题
Multi-output Gaussian process prediction for computationally expensive problems with multiple levels of fidelity
作者
关键词
Surrogate model, Multi-fidelity, Multi-output Gaussian process, Computationally expensive problems
出版物
KNOWLEDGE-BASED SYSTEMS
Volume 227, Issue -, Pages 107151
出版商
Elsevier BV
发表日期
2021-05-26
DOI
10.1016/j.knosys.2021.107151
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Optimization of expensive black-box problems via Gradient-enhanced Kriging
- (2020) Liming Chen et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- A generalized hierarchical co-Kriging model for multi-fidelity data fusion
- (2020) Qi Zhou et al. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
- An active-learning method based on multi-fidelity Kriging model for structural reliability analysis
- (2020) Jiaxiang Yi et al. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
- MOGPTK: The multi-output Gaussian process toolkit
- (2020) Taco de Wolff et al. NEUROCOMPUTING
- Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach
- (2020) Raed Kontar et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids
- (2019) Zhonghua Han et al. Chinese Journal of Aeronautics
- Multi-fidelity uncertainty propagation using polynomial chaos and Gaussian process modeling
- (2019) Fenggang Wang et al. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
- A two-stage adaptive multi-fidelity surrogate model-assisted multi-objective genetic algorithm for computationally expensive problems
- (2019) Qi Zhou et al. ENGINEERING WITH COMPUTERS
- Linear regression-based multifidelity surrogate for disturbance amplification in multiphase explosion
- (2019) M. Giselle Fernández-Godino et al. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
- Gaussian Process Regression for numerical wind speed prediction enhancement
- (2019) Haoshu Cai et al. RENEWABLE ENERGY
- Cope with diverse data structures in multi-fidelity modeling: A Gaussian process method
- (2018) Haitao Liu et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Remarks on multi-output Gaussian process regression
- (2018) Haitao Liu et al. KNOWLEDGE-BASED SYSTEMS
- A screening-based gradient-enhanced Kriging modeling method for high-dimensional problems
- (2018) Liming Chen et al. APPLIED MATHEMATICAL MODELLING
- Metamodeling for high dimensional design problems by multi-fidelity simulations
- (2017) Xiwen Cai et al. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
- SU2: An Open-Source Suite for Multiphysics Simulation and Design
- (2016) Thomas D. Economon et al. AIAA JOURNAL
- Power in Simplicity with ASM: Tracing the Aggressive Space Mapping Algorithm Over Two Decades of Development and Engineering Applications
- (2016) Jose E. Rayas-Sanchez IEEE MICROWAVE MAGAZINE
- Multitask Gaussian Processes for Multivariate Physiological Time-Series Analysis
- (2015) Robert Durichen et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function
- (2012) Zhong-Hua Han et al. AEROSPACE SCIENCE AND TECHNOLOGY
- Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling
- (2012) Zhong-Hua Han et al. AIAA JOURNAL
- Space-filling Latin hypercube designs for computer experiments
- (2010) Bart G. M. Husslage et al. OPTIMIZATION AND ENGINEERING
- JADE: Adaptive Differential Evolution With Optional External Archive
- (2009) Jingqiao Zhang et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
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