Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression
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Publication:2174145
DOI10.1016/j.cma.2019.02.021zbMath1441.60003OpenAlexW2920434758MaRDI QIDQ2174145
Kai Cheng, Ying Zhen, Zhen-Zhou Lü
Publication date: 20 April 2020
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cma.2019.02.021
Gaussian processes (60G15) Computational methods for problems pertaining to probability theory (60-08) Numerical solutions to stochastic differential and integral equations (65C30)
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Efficient uncertainty quantification of stochastic problems in CFD by combination of compressed sensing and POD-kriging ⋮ Stochastic field representation using bi-fidelity combination of proper orthogonal decomposition and kriging ⋮ Enhanced variable-fidelity surrogate-based optimization framework by Gaussian process regression and fuzzy clustering ⋮ Stochastic analysis of structures under limited observations using kernel density estimation and arbitrary polynomial chaos expansion ⋮ Efficient uncertainty quantification of CFD problems by combination of proper orthogonal decomposition and compressed sensing
Uses Software
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