An ensemble Monte Carlo HDG method for parabolic PDEs with random coefficients

被引:3
|
作者
Li, Meng [1 ]
Luo, Xianbing [1 ]
机构
[1] Guizhou Univ, Sch Math & Stat, Guiyang 550025, Guizhou, Peoples R China
基金
中国国家自然科学基金;
关键词
Parabolic PDEs; random coefficients; HDG method; ensemble; Monte Carlo; PARTIAL-DIFFERENTIAL-EQUATIONS; STOCHASTIC COLLOCATION METHOD; ERROR ANALYSIS; ALGORITHM; GALERKIN; SCHEME; GMRES;
D O I
10.1080/00207160.2022.2119082
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
We use Monte Carlo, ensemble and hybrid discontinuous Galerkin method (EMC-HDG) to numerically solve parabolic partial differential equations (PDEs) with random coefficients. The proposed method reduces the computational cost and the storage requirement by solving multiple linear systems with a common coefficient matrix. Error analysis shows the proposed method is first-order accurate in time and optimal L-2 convergence order in physical space. In the end, several numerical experiments are presented to verify the theoretical results.
引用
收藏
页码:405 / 421
页数:17
相关论文
共 50 条
  • [31] Quasi-Monte Carlo integration with product weights for elliptic PDEs with log-normal coefficients
    Kazashi, Yoshihito
    IMA JOURNAL OF NUMERICAL ANALYSIS, 2019, 39 (03) : 1563 - 1593
  • [32] A MULTISCALE DATA-DRIVEN STOCHASTIC METHOD FOR ELLIPTIC PDEs WITH RANDOM COEFFICIENTS
    Zhang, Zhiwen
    Ci, Maolin
    Hou, Thomas Y.
    MULTISCALE MODELING & SIMULATION, 2015, 13 (01) : 173 - 204
  • [33] Multilevel Monte Carlo method for parabolic stochastic partial differential equations
    Barth, Andrea
    Lang, Annika
    Schwab, Christoph
    BIT NUMERICAL MATHEMATICS, 2013, 53 (01) : 3 - 27
  • [34] DEEP SPLITTING METHOD FOR PARABOLIC PDEs
    Beck, Christian
    Becker, Sebastian
    Cheridito, Patrick
    Jentzen, Arnulf
    Neufeld, Ariel
    SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2021, 43 (05) : A3135 - A3154
  • [35] Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs
    Neufeld, Ariel
    Schmocker, Philipp
    Wu, Sizhou
    COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2025, 143
  • [36] Calculating the threshold of random laser by using Monte Carlo method
    Fu Fang-Zheng
    Li Ming
    ACTA PHYSICA SINICA, 2009, 58 (09) : 6258 - 6263
  • [37] Conditional Monte Carlo method for dynamic systems with random properties
    Grigoriu, Mircea
    APPLIED MATHEMATICAL MODELLING, 2012, 36 (03) : 1209 - 1218
  • [38] Monte Carlo Based Ensemble Forecasting
    L. Mark Berliner
    Statistics and Computing, 2001, 11 : 269 - 275
  • [39] Ensemble Denoising for Monte Carlo Renderings
    Zheng, Shaokun
    Zheng, Fengshi
    Xu, Kun
    Yan, Ling-Qi
    ACM TRANSACTIONS ON GRAPHICS, 2021, 40 (06):
  • [40] Monte Carlo based ensemble forecasting
    Berliner, LM
    STATISTICS AND COMPUTING, 2001, 11 (03) : 269 - 275