Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark
METADATA ONLY
Loading...
Author / Producer
Date
2021
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful sparse regression solvers to approximate computer models with many input parameters, relying on only a few model evaluations. Within the last decade, a large number of algorithms for the computation of sparse PCE have been published in the applied math and engineering literature. We present an extensive review of the existing methods and develop a framework for classifying the algorithms. Furthermore, we conduct a unique benchmark on a selection of methods to identify which approaches work best
in practical applications. Comparing their accuracy on several benchmark models of varying dimensionality and complexity, we find that the choice of sparse regression solver and sampling scheme for the computation of a sparse PCE surrogate can make a significant difference of up to several orders of magnitude in the resulting mean-squared error. Different methods seem to be superior in different regimes of model dimensionality and experimental design size.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
9 (2)
Pages / Article No.
593 - 649
Publisher
SIAM
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Uncertainty Quantification; Surrogate modelling; Sparse regression; Sparse polynomial chaos expansions; Experimental design
Organisational unit
03962 - Sudret, Bruno / Sudret, Bruno
Notes
Funding
175524 - Surrogate Modelling for Stochastic Simulators (SAMOS) (SNF)
Related publications and datasets
Is referenced by: