Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation

DOI10.1016/j.jcp.2016.05.039zbMath1349.65049arXiv1602.04550OpenAlexW2282795067MaRDI QIDQ726924

Ilias Bilionis, Marcial Gonzalez, Rohit K. Tripathy

Publication date: 5 December 2016

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1602.04550

zbMATH Keywords

Stiefel manifold dimensionality reduction uncertainty quantification Gaussian process regression active subspace granular crystals

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15) General nonlinear regression (62J02)

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