Matlab latin hypercube sampling gumbel distribution
(1996), Monte Carlo: Concepts, Algorithms, Applications, Springer, New York (First edition published by John Wiley & Sons Ltd., Chichester, 1996, ISBN 6 1), Internet edition 2.2.5 (1998), "Stratified random sampling for power estimation", IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems, 17(6), 465-471 (1996), "Stratified random sampling for power estimation", Iccad 00, 576 (1989), "Simulation of seismic ground motion using stochastic waves", J. (2005), "Hybrid subset simulation method for reliability estimation of dynamical systems subject to stochastic excitation", Probabilist.
#Matlab latin hypercube sampling gumbel distribution series
(1947), "The orthogonal development of nonlinear functionals in series of fourierhermite functionals", Ann. (1996), "Generation of non-gaussian stationary processes", Phys. (2003), "Important sampling in high dimensions", Struct. (2001b), "Estimation of small failure probabilities in high dimensions by subset simulation", Probabilist. (2007), "Application of subset simulation methods to reliability benchmark problems", Struct. (2001a), "First excursion probabilities for linear systems by very efficient importance sampling", Probabilist. The numerical example demonstrates the applicability of Line Sampling to general linear uncertain FE systems under Gaussian distributed excitation. After discussing Direct Monte Carlo Simulation for the assessment of the response variability, some recently developed advanced Monte Carlo methods applied for reliability assessment are described, such as Importance Sampling for linear uncertain structures subjected to Gaussian loading, Line Sampling in linear dynamics and Subset simulation. The general applicability and versatility of Monte Carlo Simulation is demonstrated in the context with computational models that have been developed for deterministic structural analysis. Monte Carlo methods are proposed for studying the variability in the structural properties and for their propagation to the response. Presently available procedures to describe uncertainties in load and resistance within a suitable mathematical framework are shortly addressed.
The present contribution addresses uncertainty quantification and uncertainty propagation in structural mechanics using stochastic analysis.