COMPARISON OF NUMERICAL METHODS FOR UNCERTAINTY QUANTIFICATION

Abstract eng:
An extensive development of efficient methods for stochastic modelling enabled uncertainty propagation through complex models. In this contribution, we present a review and comparison of several approaches such as stochastic Galerkin method, stochastic collocation method or polynomial regression based on Latin Hypercube Sampling. The advantages and disadvantages of these methods are demonstrated within the comparison with the traditional Monte Carlo method on a simple illustrative example of a frame structure.

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