Efficient Monte Carlo algorithm for rare failure event simulation Patelli, Edoardo; Au, Siu Kui
Studying failure scenarios allows one to gain insights into their cause and consequence, providing information for effective mitigation, contingency planning and improving system resilience. A new efficient algorithm is here proposed to solve applications where an expensive-to-evaluate computer model is involved. The algorithms allows to generate the conditional samples for the Subset simulation by representing each random variable by an arbitrary number of hidden variables. The resulting algorithm is very simple yet powerful and it does not required the use of the Markov Chain Monte Carlo method. The proposed algorithm has been implemented in a open source general purpose software, OpenCossan allowing the solution of large scale problems of industrial interest by taking advantages of High Performance Computing facilities. The applicability and flexibility of the proposed approach is shown by solving a number of different problems.
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