A sparse multiresolution stochastic approximation for uncertainty quantification

被引：5
|
作者：
Schiavazzi, D. [1 ]
Doostan, A.
Iaccarino, G.
机构：
[1] Univ Padua, Dipartimento Matemat, I-35100 Padua, Italy
来源：
RECENT ADVANCES IN SCIENTIFIC COMPUTING AND APPLICATIONS | 2013年 / 586卷
关键词：
DIFFERENTIAL-EQUATIONS; POLYNOMIAL CHAOS;
D O I：
10.1090/conm/586/11634
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
The present work proposes a novel sampling-based uncertainty propagation framework in which solutions are represented using a multiresolution dictionary. The coefficients of such an expansion are evaluated using greedy methodologies within the Compressive Sampling framework. The effect of various sampling strategies is investigated. The proposed methodology is verified on the Kraichnan-Orszag problem with one and two random initial conditions.
引用
收藏
页码：295 / +
页数：3
相关论文
共 50 条
  • [31] Uncertainty quantification for stochastic dynamical systems using time-dependent stochastic bases
    Lan, Jinchun
    Zhang, Qianlong
    Wei, Sha
    Peng, Zhike
    Dong, Xinjian
    Zhang, Wenming
    APPLIED MATHEMATICS AND MECHANICS-ENGLISH EDITION, 2019, 40 (01) : 63 - 84
  • [32] A Stochastic Collocation Method for Uncertainty Quantification and Propagation in Cardiovascular Simulations
    Sankaran, Sethuraman
    Marsden, Alison L.
    JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME, 2011, 133 (03):
  • [33] Sparse polynomial surrogates for non-intrusive, high-dimensional uncertainty quantification of aeroelastic computations
    Savin, Eric
    Hantrais-Gervois, Jean-Luc
    PROBABILISTIC ENGINEERING MECHANICS, 2020, 59 (59)
  • [34] Uncertainty quantification of offshore wind farms using Monte Carlo and sparse grid
    Richter, Pascal
    Wolters, Jannick
    Frank, Martin
    ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY, 2022, 17 (01)
  • [35] Uncertainty quantification via random domain decomposition and probabilistic collocation on sparse grids
    Lin, G.
    Tartakovsky, A. M.
    Tartakovsky, D. M.
    JOURNAL OF COMPUTATIONAL PHYSICS, 2010, 229 (19) : 6995 - 7012
  • [36] Locally Refined Adaptive Sparse Surrogate-Based Approach for Uncertainty Quantification
    Chatterjee, Tanmoy
    Chowdhury, Rajib
    JOURNAL OF ENGINEERING MECHANICS, 2019, 145 (05)
  • [37] Multilevel Monte Carlo applied for uncertainty quantification in stochastic multiscale systems
    Kimaev, Grigoriy
    Chaffart, Donovan
    Ricardez-Sandoval, Luis A.
    AICHE JOURNAL, 2020, 66 (08)
  • [38] Uncertainty quantification in computational stochastic multiscale analysis of nonlinear elastic materials
    Clement, A.
    Soize, C.
    Yvonnet, J.
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2013, 254 : 61 - 82
  • [39] A MIXED UNCERTAINTY QUANTIFICATION APPROACH USING EVIDENCE THEORY AND STOCHASTIC EXPANSIONS
    Shah, Harsheel
    Hosder, Serhat
    Winter, Tyler
    INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2015, 5 (01) : 21 - 48
  • [40] Nonintrusive Uncertainty Quantification of Dynamic Power Systems Subject to Stochastic Excitations
    Qiu, Yiwei
    Lin, Jin
    Chen, Xiaoshuang
    Liu, Feng
    Song, Yonghua
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2021, 36 (01) : 402 - 414
12345