Researchers have introduced a new method for reliability analysis of systems with non-deterministic behavior, where simulations produce varying outputs even with identical inputs. The approach utilizes generalized lambda models and stochastic polynomial chaos expansions as surrogate models to efficiently learn the inherent randomness. This technique significantly reduces the computational cost compared to traditional Monte Carlo simulations, as demonstrated through case studies involving an analytical function, a beam model, and a realistic wind turbine. AI
IMPACT Introduces novel emulation techniques for uncertainty quantification in complex systems, potentially improving simulation efficiency.
RANK_REASON This is a research paper detailing a new methodology for reliability analysis. [lever_c_demoted from research: ic=1 ai=0.7]
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