Variance-based adaptive sequential sampling for polynomial chaos expansion
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Publication:2246308
DOI10.1016/j.cma.2021.114105OpenAlexW3196918406MaRDI QIDQ2246308
Václav Sadílek, Michael D. Shields, Lukáš Novák, Miroslav Vořechovský
Publication date: 16 November 2021
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.2021.114105
Linear regression; mixed models (62J05) Optimal statistical designs (62K05) Monte Carlo methods (65C05) General theory of stochastic processes (60G07) Sequential statistical design (62L05)
Related Items (5)
Stochastic analysis of structures under limited observations using kernel density estimation and arbitrary polynomial chaos expansion ⋮ An active sparse polynomial chaos expansion approach based on sequential relevance vector machine ⋮ Classifier-based adaptive polynomial chaos expansion for high-dimensional uncertainty quantification ⋮ Comparison of the performance and reliability between improved sampling strategies for polynomial chaos expansion ⋮ On the use of sparse Bayesian learning-based polynomial chaos expansion for global reliability sensitivity analysis
Uses Software
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