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On the convergence of the stochastic Galerkin method for random elliptic partial differential equations
Antje Mugler and Hans-Jörg Starkloff ESAIM: Mathematical Modelling and Numerical Analysis 47(5) 1237 (2013) https://doi.org/10.1051/m2an/2013066
Wick–Malliavin approximation to nonlinear stochastic partial differential equations: analysis and simulations
D. Venturi, X. Wan, R. Mikulevicius, B. L. Rozovskii and G. E. Karniadakis Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 469(2158) 20130001 (2013) https://doi.org/10.1098/rspa.2013.0001
Approximation of Quantities of Interest in Stochastic PDEs by the Random Discrete $L^2$ Projection on Polynomial Spaces
G. Migliorati, F. Nobile, E. von Schwerin and R. Tempone SIAM Journal on Scientific Computing 35(3) A1440 (2013) https://doi.org/10.1137/120897109
Constructing Nested Nodal Sets for Multivariate Polynomial Interpolation
Measure transformation and efficient quadrature in reduced‐dimensional stochastic modeling of coupled problems
M. Arnst, R. Ghanem, E. Phipps and J. Red‐Horse International Journal for Numerical Methods in Engineering 92(12) 1044 (2012) https://doi.org/10.1002/nme.4368
ON THE OPTIMAL POLYNOMIAL APPROXIMATION OF STOCHASTIC PDES BY GALERKIN AND COLLOCATION METHODS
JOAKIM BECK, RAUL TEMPONE, FABIO NOBILE and LORENZO TAMELLINI Mathematical Models and Methods in Applied Sciences 22(09) (2012) https://doi.org/10.1142/S0218202512500236
Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion