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An adaptive data-driven subspace polynomial dimensional decomposition for high-dimensional uncertainty quantification based on maximum entropy method and sparse Bayesian learning

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Date of Publication:2024-04-24

Journal:Structural Safety

Volume:108

ISSN No.:0167-4730

Key Words:Bayesian learning; Budget control; Computation theory; Data driven; Data-driven methods; High-dimensional; Learning algorithms; Learning systems; Maximum entropy methods; Maximum-entropy methods; Metadata; Polynomial dimensional decomposition; Probability density function; Probability distributions; Random variables; Sparse bayesian; Sparse bayesian learning; Subspace method; Uncertainty analysis; Uncertainty quantifications

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An adaptive data-driven subspace polynomial dimensional decomposition for high-dimensional uncertainty quantification based on maximum entropy method and sparse Bayesian learning