Stochastic simulation
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure
SIAM Journal on Scientific Computing
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Splitting for rare-event simulation
Proceedings of the 38th conference on Winter simulation
Rare events, splitting, and quasi-Monte Carlo
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Adaptive sparse polynomial chaos expansion based on least angle regression
Journal of Computational Physics
Hi-index | 0.00 |
In the context of structural reliability, a small probability to be assessed, a high computational time model and a relatively large input dimension are typical constraints which brought together lead to an interesting challenge. Indeed, in this framework many existing stochastic methods fail in estimating the failure probability with robustness.Therefore, the aim of this article is to present and prove theoretical results about the validity of an original method we have introduced to overcome the specific constraints mentioned above. This new method turns out to be competitive compared with the existing techniques. It is a variant of accelerated Monte Carlo simulation method, named ADS-2--Adaptive Directional Stratification. It combines, in a two steps adaptive strategy, the stratification into quadrants and the directional simulation techniques. Two ADS-2 estimators are presented and their properties are studied.