An efficient sparse Bayesian learning framework for stochastic response analysis

The computational intensiveness inherently associated with uncertainty quantification of engineering systems has been one of the prime concerns over the years. In order to mitigate this issue, a novel approach has been developed for efficient stochastic computations. The proposed approach has been developed by amalgamating the advantages of two available techniques namely, high dimensional model representation (HDMR) and Kriging. These two methods are coupled in such a way that HDMR addresses the global variation in the functional space using a set of component functions and the fine aberrations are interpolated by utilizing Kriging, performing as a two level approximation. A Bayesian learning framework has been integrated with the locally refined model so as to construct a sparse configuration. Implementation of the proposed approach has been demonstrated with five benchmark problems and a practical offshore structural problem. The efficiency and accuracy of the proposed approach in stochastic response analysis have been assessed by comparison with Monte Carlo simulation. Excellent results in terms of accuracy and computational effort obtained makes the proposed methodology potential for further complex applications.