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

[31] Uncertainty Quantification and Propagation in Computational Materials Science and Simulation-Assisted Materials Design
Honarmandi, Pejman
Arroyave, Raymundo
INTEGRATING MATERIALS AND MANUFACTURING INNOVATION, 2020, 9 (01) : 103 - 143
[32] Accelerating model evaluations in uncertainty propagation on tensor grids using computational graph transformations
Wang, Bingran
Sperry, Mark
Gandarillas, Victor E.
Hwang, John T.
AEROSPACE SCIENCE AND TECHNOLOGY, 2024, 145
[33] B-splines on sparse grids for surrogates in uncertainty quantification
Rehme, Michael F.
Franzelin, Fabian
Pflueger, Dirk
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2021, 209
[34] Multiaxial fatigue prediction and uncertainty quantification based on back propagation neural network and Gaussian process regression
Gao, Jingjing
Wang, Jun
Xu, Zili
Wang, Cunjun
Yan, Song
INTERNATIONAL JOURNAL OF FATIGUE, 2023, 168
[35] Multidirectional Gaussian Mixture Models for Nonlinear Uncertainty Propagation
Vittaldev, V.
Russell, R. P.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, 2016, 111 (01): : 83 - 117
[36] A Flexible Uncertainty Propagation Framework for General Multiphysics Systems
Mittal, A.
Chen, X.
Tong, A. H.
Iaccarino, G.
SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 2016, 4 (01): : 218 - 243
[37] Uncertainty propagation in risk and resilience analysis of hierarchical systems
Tabandeh, Armin
Sharma, Neetesh
Gardoni, Paolo
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2022, 219
[38] Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
Peherstorfer, Benjamin
Willcox, Karen
Gunzburger, Max
SIAM REVIEW, 2018, 60 (03) : 550 - 591
[39] Uncertainty propagation in CFD using polynomial chaos decomposition
Knio, O. M.
Le Maitre, O. P.
FLUID DYNAMICS RESEARCH, 2006, 38 (09) : 616 - 640
[40] Uncertainty propagation in orbital mechanics via tensor decomposition
Sun, Yifei
Kumar, Mrinal
CELESTIAL MECHANICS & DYNAMICAL ASTRONOMY, 2016, 124 (03) : 269 - 294

← 1 2 3 4 5 →