Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets

Abstract

We develop a framework for multifidelity information fusion and predictive inference in high-dimensional input spaces and in the presence of massive data sets. Hence, we tackle simultaneously the “big N” problem for big data and the curse of dimensionality in multivariate parametric problems. The proposed methodology establishes a new paradigm for… (More)
DOI: 10.1137/15M1055164

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Cite this paper

@article{Perdikaris2016MultifidelityIF, title={Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets}, author={Paris Perdikaris and Daniele Venturi and George Em Karniadakis}, journal={SIAM J. Scientific Computing}, year={2016}, volume={38} }