SPARSE MULTIRESOLUTION REGRESSION FOR UNCERTAINTY PROPAGATION

被引：13

作者：

Schiavazzi, Daniele ^{[1
]}

Doostan, Alireza ^{[2
]}

Iaccarino, Gianluca ^{[3
]}

机构：

[1] Univ Calif San Diego, Dept Aerosp Engn & Mech, La Jolla, CA 92093 USA

[2] Univ Colorado, Aerosp Engn Sci Dept, Boulder, CO 80309 USA

[3] Stanford Univ, Dept Engn Mech, Stanford, CA 94305 USA

来源：

INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION | 2014年 / 4卷 / 04期

基金：

美国国家科学基金会;

关键词：

uncertainty quantification; multiresolution approximation; compressive sampling; adaptive importance sampling; tree-based orthogonal matching pursuit; uncertain tuned mass damper; PARTIAL-DIFFERENTIAL-EQUATIONS; STOCHASTIC COLLOCATION METHOD; POLYNOMIAL CHAOS; SIGNAL RECOVERY; APPROXIMATION; BASES; MINIMIZATION; SYSTEMS;

D O I：

10.1615/Int.J.UncertaintyQuantification.2014010147

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：303 / 331

页数：29

共 50 条

[21] A sparse multiresolution technique for fast capacitance computation
Jandhyala, V
Michielssen, E
Mittra, R
MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, 1996, 11 (05) : 242 - 247
[22] Sparse multiresolution technique for fast capacitance computation
Univ of Illinois, Urbana, United States
Microwave Opt Technol Lett, 5 (242-247):
[23] Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior
Travis, Luke
Ray, Kolyan
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
[24] Uncertainty Analysis and Experimental Validation of Identifying the Governing Equation of an Oscillator Using Sparse Regression
Ren, Yaxiong
Adams, Christian
Melz, Tobias
APPLIED SCIENCES-BASEL, 2022, 12 (02):
[25] Sparse regression Chebyshev polynomial interval method for nonlinear dynamic systems under uncertainty
Li, Chao
Chen, Biaosong
Peng, Haijun
Zhang, Sheng
APPLIED MATHEMATICAL MODELLING, 2017, 51 : 505 - 525
[26] Regression-based sparse polynomial chaos for uncertainty quantification of subsurface flow models
Alexander, Tarakanov
Ahmed, Elsheikh H.
JOURNAL OF COMPUTATIONAL PHYSICS, 2019, 399
[27] Analytical results for uncertainty propagation through trained machine learning regression models
Thompson, Andrew
MEASUREMENT, 2024, 234
[28] Parametric Sparse Bayesian Dictionary Learning for Multiple Sources Localization With Propagation Parameters Uncertainty
You, Kangyong
Guo, Wenbin
Peng, Tao
Liu, Yueliang
Zuo, Peiliang
Wang, Wenbo
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2020, 68 : 4194 - 4209
[29] Sparse Approximation Based Gaussian Mixture Model Approach for Uncertainty Propagation for Nonlinear Systems
Vishwajeet, Kumar
Singla, Puneet
2013 AMERICAN CONTROL CONFERENCE (ACC), 2013, : 1213 - 1218
[30] Sparse Regression as a Sparse Eigenvalue Problem
Moghaddam, Baback
Gruber, Amit
Weiss, Yair
Avidan, Shai
2008 INFORMATION THEORY AND APPLICATIONS WORKSHOP, 2008, : 255 - +

← 1 2 3 4 5 →