Iterative simulation for stochastically nonlinear large variability

Monte Carlo simulation can be accelerated for material stochasticity when t he samples are ordered according to their closeness in the constitutive mod uli. Iteration on the previous solution is proposed with the samples organi zed in descending order according to a stiffness norm. A proof of unconditi onal convergence is established here. A formal definition of stochastic non linearity is derived to characterize large variability. iterations will div erge for a such case when the computation for the ensemble is initiated wit h average parameters. In reliability analysis this stochastic nonlinearity is independent of the familiar constitutive and kinematic nonlinearities. T he present methodology makes large scale Monte Carlo simulations economical ly feasible for practical design-analysis.