Identification of polynomial chaos representations in high dimension
type: Dataset
description: The usual identification methods of polynomial chaos expansions in high dimension are based on the use of a series of truncations that induce numerical bias. We first quantify the detrimental influence of this numerical bias, we then propose a new decomposition of the polynomial chaos coefficients to allow performing relevant convergence analysis and identification with respect to an arbitrary measure for the high dimension case.
issued: 2026-05-16T23:12:42.106679
language: en
modified: 2026-05-16T23:12:42.106688
contact point: Organization
  fn: CCSD
keyword: conference-papers
keyword: infoeu-reposemanticsconferenceobject
keyword: mathmath-prmathematics-mathprobability-mathpr
keyword: mathmath-stmathematics-mathstatistics-mathst
keyword: spimecaengineering-sciences-physicsmechanics-physicsmed-ph
keyword: uncertainty-quantification
landing page: SIAM Conference on Uncertainty Quantification

Identification of polynomial chaos representations in high dimension
type: Distribution
format: HTML
issued: 2026-05-16T23:12:42.183561
modified: 2026-05-16T23:12:42.080067
access URL: https://hal.science/hal-00701619

test_moissonnage_selune
type: Agent