年間 6 号発行
ISSN 印刷: 2152-5080
ISSN オンライン: 2152-5099
Indexed in
SPARSE MULTIRESOLUTION REGRESSION FOR UNCERTAINTY PROPAGATION
要約
The present work proposes a novel nonintrusive, i.e., sampling-based, framework for approximating stochastic solutions of interest admitting sparse multiresolution expansions. The coefficients of such expansions are computed via greedy approximation techniques that require a number of solution realizations smaller than the cardinality of the multiresolution basis. The effect of various random sampling strategies is investigated. The proposed methodology is verified on a number of benchmark problems involving nonsmooth stochastic responses, and is applied to quantifying the efficiency of a passive vibration control system operating under uncertainty.
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Hadigol Mohammad, Maute Kurt, Doostan Alireza, On uncertainty quantification of lithium-ion batteries: Application to an LiC6/LiCoO2 cell, Journal of Power Sources, 300, 2015. Crossref
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Schiavazzi D.E., Doostan A., Iaccarino G., Marsden A.L., A generalized multi-resolution expansion for uncertainty propagation with application to cardiovascular modeling, Computer Methods in Applied Mechanics and Engineering, 314, 2017. Crossref
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Figueroa C. Alberto, Taylor Charles A., Marsden Alison L., Blood Flow, in Encyclopedia of Computational Mechanics Second Edition, 2017. Crossref
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Xu Juan, Zhang Jianjun, Sun Chunyu, Dong Jianghui, Feature extraction of vibration signal using OMP-NWE method, Journal of Vibroengineering, 19, 3, 2017. Crossref
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Couaillier Vincent, Savin Éric, Generalized Polynomial Chaos for Non-intrusive Uncertainty Quantification in Computational Fluid Dynamics, in Uncertainty Management for Robust Industrial Design in Aeronautics, 140, 2019. Crossref
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Hampton Jerrad, Doostan Alireza, Basis adaptive sample efficient polynomial chaos (BASE-PC), Journal of Computational Physics, 371, 2018. Crossref
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Diaz Paul, Doostan Alireza, Hampton Jerrad, Sparse polynomial chaos expansions via compressed sensing and D-optimal design, Computer Methods in Applied Mechanics and Engineering, 336, 2018. Crossref
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Pettersson Per, Doostan Alireza, Nordström Jan, Level set methods for stochastic discontinuity detection in nonlinear problems, Journal of Computational Physics, 392, 2019. Crossref
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Hampton Jerrad, Doostan Alireza, Compressive Sampling Methods for Sparse Polynomial Chaos Expansions, in Handbook of Uncertainty Quantification, 2015. Crossref
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Kougioumtzoglou Ioannis A., Petromichelakis Ioannis, Psaros Apostolos F., Sparse representations and compressive sampling approaches in engineering mechanics: A review of theoretical concepts and diverse applications, Probabilistic Engineering Mechanics, 61, 2020. Crossref
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Seo Jongmin, Schiavazzi Daniele E., Kahn Andrew M., Marsden Alison L., The effects of clinically‐derived parametric data uncertainty in patient‐specific coronary simulations with deformable walls, International Journal for Numerical Methods in Biomedical Engineering, 36, 8, 2020. Crossref
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Inoue Takumi, Miyaji Koji, Non-Intrusive Uncertainty Quantification Method for Flows with Discontinuity, AIAA Scitech 2020 Forum, 2020. Crossref
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Savin Éric, Hantrais-Gervois Jean-Luc, Sparse polynomial surrogates for non-intrusive, high-dimensional uncertainty quantification of aeroelastic computations, Probabilistic Engineering Mechanics, 59, 2020. Crossref
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Wang Yu, Liu Fang, Schiavazzi Daniele E., Variational inference with NoFAS: Normalizing flow with adaptive surrogate for computationally expensive models, Journal of Computational Physics, 467, 2022. Crossref