An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework

DOI10.1016/j.cma.2019.04.046zbMath1441.62201OpenAlexW2946328771WikidataQ127836919 ScholiaQ127836919MaRDI QIDQ1988073

Yicheng Zhou, Chunyan Ling, Kai Cheng, Zhen-Zhou Lü

Publication date: 16 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.2019.04.046

zbMATH Keywords

sparse Bayesian learningpolynomial chaos expansionadaptive sampling methodexpectation of loss function

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Optimal statistical designs (62K05) Bayesian inference (62F15)

Related Items

Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square ⋮ Optimal sparse polynomial chaos expansion for arbitrary probability distribution and its application on global sensitivity analysis ⋮ A novel sparse polynomial chaos expansion technique with high adaptiveness for surrogate modelling ⋮ An active sparse polynomial chaos expansion approach based on sequential relevance vector machine ⋮ Classifier-based adaptive polynomial chaos expansion for high-dimensional uncertainty quantification ⋮ Variance-based adaptive sequential sampling for polynomial chaos expansion ⋮ A hybrid sequential sampling strategy for sparse polynomial chaos expansion based on compressive sampling and Bayesian experimental design ⋮ Adaboost-based ensemble of polynomial chaos expansion with adaptive sampling ⋮ Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark

Uses Software

• SPACE
• UQLab

Cites Work

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