Itô SDE--based Generator for a Class of Non-Gaussian Vector-valued Random Fields in Uncertainty Quantification

DOI10.1137/130948586zbMath1308.74166OpenAlexW2035886964MaRDI QIDQ5176050

Johann Guilleminot, Christian Soize

Publication date: 2 March 2015

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://hal-upec-upem.archives-ouvertes.fr/hal-01100173/file/SIAM-SISC-2014-Guilleminot-Soize.pdf

zbMATH Keywords

random fieldadaptive algorithmItô stochastic differential equationuncertainty quantification

Mathematics Subject Classification ID

Random fields (60G60) Probabilistic models, generic numerical methods in probability and statistics (65C20) Monte Carlo methods (65C05) Diffusion processes (60J60) Numerical solutions to stochastic differential and integral equations (65C30) Stochastic and other probabilistic methods applied to problems in solid mechanics (74S60)

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Approximate Solutions of Lagrange Multipliers for Information-Theoretic Random Field Models ⋮ Stochastic modeling of the Ogden class of stored energy functions for hyperelastic materials: the compressible case ⋮ Tensor- and spinor-valued random fields with applications to continuum physics and cosmology ⋮ Itô-SDE MCMC method for Bayesian characterization of errors associated with data limitations in stochastic expansion methods for uncertainty quantification ⋮ Functional approximation and projection of stored energy functions in computational homogenization of hyperelastic materials: a probabilistic perspective

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