Nested polynomial trends for the improvement of Gaussian process-based predictors

DOI10.1016/j.jcp.2017.05.051zbMath1378.62092OpenAlexW2338398729MaRDI QIDQ1691892

Sophie Marque-Pucheu, Christian Soize, Guillaume Perrin, Josselin Garnier

Publication date: 25 January 2018

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

Full work available at URL: https://hal-upec-upem.archives-ouvertes.fr/hal-01562655/file/publi-2017-JCP-346%28%29389-402-perrin-soize-marque-garnier-preprint.pdf

zbMATH Keywords

Gaussian processcomputer experimentBayesian frameworknested polynomial trend

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Gaussian processes (60G15)

Related Items (4)

Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework ⋮ Polynomial-chaos-based conditional statistics for probabilistic learning with heterogeneous data applied to atomic collisions of helium on graphite substrate ⋮ Systems of Gaussian process models for directed chains of solvers ⋮ Efficient sequential experimental design for surrogate modeling of nested codes

Uses Software

• BRENT

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Multi-output separable Gaussian process: towards an efficient, fully Bayesian paradigm for uncertainty quantification
• Proper generalized decompositions and separated representations for the numerical solution of high dimensional stochastic problems
• Adaptive sparse polynomial chaos expansion based on least angle regression
• Cases for the nugget in modeling computer experiments
• Sequential design of computer experiments for the estimation of a probability of failure
• Polynomial meta-models with canonical low-rank approximations: numerical insights and comparison to sparse polynomial chaos expansions
• A new surrogate modeling technique combining Kriging and polynomial chaos expansions - application to uncertainty analysis in computational dosimetry
• Enhancing \(\ell_1\)-minimization estimates of polynomial chaos expansions using basis selection
• Identification of Bayesian posteriors for coefficients of chaos expansions
• Polynomial chaos representation of spatio-temporal random fields from experimental measurements
• The design and analysis of computer experiments.
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• Least angle regression. (With discussion)
• Bayesian Calibration of Computer Models
• A Posteriori Error and Optimal Reduced Basis for Stochastic Processes Defined by a Finite Set of Realizations
• An Approach to Time Series Analysis
• Polynomial Chaos in Stochastic Finite Elements
• Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure
• Identification of Polynomial Chaos Representations in High Dimension from a Set of Realizations
• Simulation and the Monte Carlo Method
• Uncertainty propagation in CFD using polynomial chaos decomposition

This page was built for publication: Nested polynomial trends for the improvement of Gaussian process-based predictors