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Non-parametric warping via local scale estimation for non-stationary Gaussian process modelling
Author(s): Sébastien Marmin; Jean Baccou; Jacques Liandrat; David Ginsbourger
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Paper Abstract

We tackle the problem of reconstructing functions possessing highly heterogeneous behaviour across the input space from scattered evaluations. Our main approach combines non-stationary Gaussian process (GP) modelling with wavelet local analysis. A warped GP model is assumed, and a novel stationarization algorithm is proposed that relies on successive inverse warpings based on local scale estimation. The approach is applied to two mechanical case studies highlighting promising prediction performance compared to state-of-the-art methods.

Paper Details

Date Published: 24 August 2017
PDF: 10 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 1039421 (24 August 2017); doi: 10.1117/12.2272408
Show Author Affiliations
Sébastien Marmin, Institut de Radioprotection et de Sûreté Nucléaire (France)
Univ. of Bern (Switzerland)
Ctr. Marseille, I2M, Aix-Marseille Univ., CNRS (France)
Jean Baccou, Institut de Radioprotection et de Sûreté Nucléaire (France)
Lab. de Micromécanique et d'Intégrité des Structures, IRSN-CNRS-UM (France)
Jacques Liandrat, Ctr. Marseille, I2M, Aix-Marseille Univ., CNRS (France)
David Ginsbourger, Idiap Research Institute (Switzerland)
Univ. Bern (Switzerland)


Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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