Quasi-random numbers in stochastic finite element analysis

49 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Recent Developments in Spectral Stochastic Methods for the Numerical Solution of Stochastic Partial Differential Equations (2009, methodology): “L2 Projection The L2 projection method [18, 48, 71, 72, 104] consists in defining the approximation (14) as the projection of J on the sub-space of L2( , dPξ ) spanned by functions {Hα}α∈IP , the projection being defined with respect to the natural inner product in L2( , dPξ )4: 〈v,w〉L2( ,dPξ ) =E(v”
Least squares polynomial chaos expansion: A review of sampling strategies (2017, methodology): “…convergence rate of standard MC technique, especially for high-dimensional integrations in ﬁnance applications [120], researchers have proposed a deterministic version of the MC sampling method based on the low-discrepancy sequences [26] known as quasi-Monte Carlo (QMC) technique [89, 14, 8, 79].”
An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis (2010, background): “, 6) [28], whose mean and standard deviation are reported in Table 3.”