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- W2735172663 abstract "Due to its simplicity and good statistical results, the Monte Carlo (MC) method is the most commonly technique used for uncertainty quantification. However, its computational cost is significant, and, in many cases, prohibitive. Fortunately, the MC algorithm can be can be parallelized, which may allows its use in complex simulations. In this sprit, this work presents a methodology for the parallelization of MC method in a cloud computing setting. The methodology is illustrated on a stochastic problem of structural dynamics, and the simulation results show good accuracy for low-order statistics. Also, this methodology shows a good performance in terms of processing-time and storage space usage." @default.
- W2735172663 created "2017-07-21" @default.
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- W2735172663 date "2013-11-03" @default.
- W2735172663 modified "2023-09-25" @default.
- W2735172663 title "Uncertainty quantification using cloud computing for Monte Carlo parallelization" @default.
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