On advanced Monte Carlo simulation procedures in stochastic structural dynamics
Monte Carlo methods, applicable to Markov processes, are modified by leading the generated samples towards the low probability range which is practically not accessible by direct Monte Carlo methods. In this context, notions as developed for variance reduction techniques are introduced and discussed. Based on criteria denoting those realizations which lead most likely to failure, a numerical procedure called ``Double \& Clump'' (D \& C) is discussed briefly. It is shown that the rather numerically involved D \& C procedure can be simplified by a technique known as ``Russian roulette and splitting''. In two numerical examples, both procedures are compared with direct Monte Carlo simulation as well as with the response surface method demonstrating comparable accuracy.
scientific article; zbMATH DE number 1079936