Surrogate Models for Oscillatory Systems Using Sparse Polynomial Chaos Expansions and Stochastic Time Warping

DOI10.1137/16M1083621zbMath1375.65005arXiv1609.09286OpenAlexW2949917305MaRDI QIDQ5269875

Chu V. Mai, Bruno Sudret

Publication date: 28 June 2017

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1609.09286

zbMATH Keywords

dynamical systemsprincipal component analysissurrogate modelsleast angle regressionsecond-order statisticsstochastic ordinary differential equationssparse polynomial chaos expansionsstochastic time warping

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Order statistics; empirical distribution functions (62G30) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Ordinary differential equations and systems with randomness (34F05) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30)

Related Items (7)

Surrogate-based parameter inference in debris flow model ⋮ Probabilistic Analysis of Highly Nonlinear Models by Adaptive Sparse Polynomial Chaos: Transient Infiltration in Unsaturated Soil ⋮ Stochastic dynamic analysis of composite plates in thermal environments using nonlinear autoregressive model with exogenous input in polynomial chaos expansion surrogate ⋮ Analytical uncertainty quantification approach based on adaptive generalized <scp>co‐Gaussian</scp> process model ⋮ Compressed Principal Component Analysis of Non-Gaussian Vectors ⋮ Intrusive generalized polynomial chaos with asynchronous time integration for the solution of the unsteady Navier-Stokes equations ⋮ Convergence Analysis and Adaptive Order Selection for the Polynomial Chaos Approach to Direct Optimal Control under Uncertainties

Uses Software

• UQLab
• OpenCourseWare

Cites Work

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