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
  • [1] A General Framework of Rotational Sparse Approximation in Uncertainty Quantification
    Hu, Mengqi
    Lou, Yifei
    Yang, Xiu
    [J]. SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 2022, 10 (04): : 1410 - 1434
  • [2] HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation
    Su, Weijie J.
    Zhu, Yuancheng
    [J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2023, 24
  • [3] SPARSE MULTIRESOLUTION REGRESSION FOR UNCERTAINTY PROPAGATION
    Schiavazzi, Daniele
    Doostan, Alireza
    Iaccarino, Gianluca
    [J]. INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2014, 4 (04) : 303 - 331
  • [4] Uncertainty Quantification for Stochastic Approximation Limits Using Chaos Expansion
    Crepey, S.
    Fort, G.
    Gobet, E.
    Stazhynski, U.
    [J]. SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 2020, 8 (03): : 1061 - 1089
  • [5] Uncertainty Quantification of Hypersonic Reentry Flows with Sparse Sampling and Stochastic Expansions
    West, Thomas K.
    Hosder, Serhat
    [J]. JOURNAL OF SPACECRAFT AND ROCKETS, 2015, 52 (01) : 120 - 133
  • [6] Uncertainty Quantification of Waveguide Dispersion Using Sparse Grid Stochastic Testing
    Gossye, Michiel
    Gordebeke, Gert-Jan
    Kapusuz, Kamil Yavuz
    Vande Ginste, Dries
    Rogier, Hendrik
    [J]. IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2020, 68 (07) : 2485 - 2494
  • [7] Uncertainty Quantification for Sparse Deep Learning
    Wang, Yuexi
    Rockova, Veronika
    [J]. INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 108, 2020, 108
  • [8] The Distribution and Uncertainty Quantification of Wind Profile in the Stochastic General Ekman Momentum Approximation Model
    Bing YAN
    Sixun HUANG
    Jing FENG
    Yu WANG
    [J]. Journal of Meteorological Research, 2019, 33 (02) : 336 - 348
  • [9] Uncertainty Quantification Using Sparse Approximation for Models With a High Number of Parameters: Application to a Magnetoelectric Sensor
    Nguyen, T. T.
    Mac, D. H.
    Clenet, S.
    [J]. IEEE TRANSACTIONS ON MAGNETICS, 2016, 52 (03)
  • [10] The Distribution and Uncertainty Quantification of Wind Profile in the Stochastic General Ekman Momentum Approximation Model
    Bing Yan
    Sixun Huang
    Jing Feng
    Yu Wang
    [J]. Journal of Meteorological Research, 2019, 33 : 336 - 348
12345