user

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
  • [21] Shape Uncertainty Quantification for Electromagnetic Wave Scattering via First-Order Sparse Boundary Element Approximation
    Escapil-Inchauspe, Paul
    Jerez-Hanckes, Carlos
    IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2024, 72 (08) : 6627 - 6637
  • [22] Computational uncertainty analysis in multiresolution materials via stochastic constitutive theory
    Greene, M. Steven
    Liu, Yu
    Chen, Wei
    Liu, Wing Kam
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2011, 200 (1-4) : 309 - 325
  • [23] Inverse uncertainty quantification of TRACE physical model parameters using sparse gird stochastic collocation surrogate model
    Wu, Xu
    Mui, Travis
    Hu, Guojun
    Meidani, Hadi
    Kozlowski, Tomasz
    NUCLEAR ENGINEERING AND DESIGN, 2017, 319 : 185 - 200
  • [24] Uncertainty quantification analysis with sparse polynomial chaos method
    Chen J.
    Zhang C.
    Liu X.
    Zhao H.
    Hu X.
    Wu X.
    Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica, 2020, 41 (03):
  • [25] Uncertainty quantification for monotone stochastic degradation models
    Chen, Piao
    Ye, Zhi-Sheng
    JOURNAL OF QUALITY TECHNOLOGY, 2018, 50 (02) : 207 - 219
  • [26] Nonparametric Uncertainty Quantification for Stochastic Gradient Flows
    Berry, Tyrus
    Harlim, John
    SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 2015, 3 (01): : 484 - 508
  • [27] Suborbital Reentry Uncertainty Quantification and Stochastic Optimization
    Berning, Andrew, Jr.
    Kehlenbeck, Andrew
    Kolmanovsky, Ilya
    Girard, Anouck
    2020 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA), 2020, : 594 - 599
  • [28] Efficient Uncertainty Quantification in Stochastic Economic Dispatch
    Safta, Cosmin
    Chen, Richard L. -Y.
    Najm, Habib N.
    Pinar, Ali
    Watson, Jean-Paul
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2017, 32 (04) : 2535 - 2546
  • [29] Stochastic approaches to uncertainty quantification in CFD simulations
    Lionel Mathelin
    M. Yousuff Hussaini
    Thomas A. Zang
    Numerical Algorithms, 2005, 38 : 209 - 236
  • [30] Spectral stochastic uncertainty quantification in chemical systems
    Reagan, MT
    Najm, HN
    Debusschere, BJ
    Le Maître, OP
    Knio, M
    Ghanem, RG
    COMBUSTION THEORY AND MODELLING, 2004, 8 (03) : 607 - 632
12345