A nonparametric probabilistic approach for quantifying uncertainties in low-dimensional and high-dimensional nonlinear models

DOI10.1002/NME.5312zbMATH Open1548.62105MaRDI QIDQ6565216

C. Farhat, C. Soize

Publication date: 1 July 2024

Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)

zbMATH Keywords

uncertainty quantificationmodel order reductionmodel uncertaintiesreduced-order modelmodeling errorsnonparametric stochastic approach

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) General nonlinear regression (62J02) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Probabilistic methods, particle methods, etc. for initial value and initial-boundary value problems involving PDEs (65M75)

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Related Items (2)

Probabilistic learning for modeling and quantifying model-form uncertainties in nonlinear computational mechanics ⋮ Stochastic symplectic reduced-order modeling for model-form uncertainty quantification in molecular dynamics simulations in various statistical ensembles

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