A generalized active subspace for dimension reduction in mixed aleatory-epistemic uncertainty quantification

DOI10.1016/j.cma.2020.113240zbMath1506.65012OpenAlexW3041437647MaRDI QIDQ2020260

Yanyan Li

Publication date: 23 April 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.2020.113240

zbMATH Keywords

dimension reductionuncertainty quantificationepistemic uncertaintyaleatory uncertaintygeneralized active subspace

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Probabilistic models, generic numerical methods in probability and statistics (65C20)

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Efficient method for fully quantifying the uncertainty of failure probability ⋮ An envelope-function-based algorithm for time-dependent reliability analysis of structures with hybrid uncertainties

Cites Work

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• A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing
• Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation
• Applications of singular-value decomposition (SVD)
• Exploiting active subspaces to quantify uncertainty in the numerical simulation of the hyshot II scramjet
• Active Subspaces
• Interval arithmetic
• The Interval Eigenvalue Problem
• Computing eigenvalue bounds of structures with uncertain‐but‐non‐random parameters by a method based on perturbation theory

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