Performance of surrogate modeling techniques in structural reliability Kroetz, Henrique M.; Beck, André T.
Solution of structural reliability and uncertainty propagation problems by Monte Carlo simulation can be a demanding task, since complex mechanical models usually have to be solved repeated times. Therefore, surrogate models are often required to reduce the computational burden. This article compares the performance of three surrogate modeling techniques in the solution of structural reliability problems. It addresses artificial neural networks, polynomial chaos and kriging meta-modeling, associated with LHS and Monte-Carlo simulation. A simple procedure for mapping input data for uncertainty quantification problems is also proposed.
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