Multi-fidelity modelling via recursive co-kriging and Gaussian-Markov random fields.

ABSTRACT: We propose a new framework for design under uncertainty based on stochastic computer simulations and multi-level recursive co-kriging. The proposed methodology simultaneously takes into account multi-fidelity in models, such as direct numerical simulations versus empirical formulae, as well as multi-fidelity in the probability space (e.g. sparse grids versus tensor product multi-element probabilistic collocation). We are able to construct response surfaces of complex dynamical systems by blending multiple information sources via auto-regressive stochastic modelling. A computationally efficient machine learning framework is developed based on multi-level recursive co-kriging with sparse precision matrices of Gaussian-Markov random fields. The effectiveness of the new algorithms is demonstra

SUBMITTER: Perdikaris P 

PROVIDER: S-EPMC4528652 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature