A Novel Weakly-Intrusive Non-linear Multiresolution Framework for Uncertainty Quantification in Hyperbolic Partial Differential Equations
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- 作者：Gianluca Geraci ; Pietro Marco Congedo ; Rémi Abgrall…
- 关键词：Multiresolution ; Uncertainty quantification ; Adaptive grid ; ENO ; MUSCL ; Hyperbolic conservation laws
- 刊名：Journal of Scientific Computing
- 出版年：2016
- 出版时间：January 2016
- 年：2016
- 卷：66
- 期：1
- 页码：358-405
- 全文大小：3,283 KB
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Pietro Marco Congedo (2)
Rémi Abgrall (3)
Gianluca Iaccarino (1)
1. Flow Physics and Computational Engineering, Mechanical Engineering Department, Stanford University, 488 Escondido Mall, Stanford, CA, 94305-3035, USA
2. INRIA Bordeaux–Sud-Ouest, 200 Avenue de la Vieille Tour, 33405, Talence Cedex, France
3. Institut für Mathematik, Universität Zürich, Winterthurerstrasse 190, 8057, Zurich, Switzerland - 刊物类别：Mathematics and Statistics
- 刊物主题：Mathematics
Algorithms
Computational Mathematics and Numerical Analysis
Applied Mathematics and Computational Methods of Engineering
Mathematical and Computational Physics - 出版者：Springer Netherlands
- ISSN：1573-7691
文摘
In this paper, a novel multiresolution framework, namely the Truncate and Encode (TE) approach, previously proposed by some of the authors (Abgrall et al. in J Comput Phys 257:19–56, 2014. doi:10.1016/j.jcp.2013.08.006), is generalized and extended for taking into account uncertainty in partial differential equations (PDEs). Innovative ingredients are given by an algorithm permitting to recover the multiresolution representation without requiring the fully resolved solution, the possibility to treat a whatever form of pdf and the use of high-order (even non-linear, i.e. data-dependent) reconstruction in the stochastic space. Moreover, the spatial-TE method is introduced, which is a weakly intrusive scheme for uncertainty quantification (UQ), that couples the physical and stochastic spaces by minimizing the computational cost for PDEs. The proposed scheme is particularly attractive when treating moving discontinuities (such as shock waves in compressible flows), even if they appear during the simulations as it is common in unsteady aerodynamics applications. The proposed method is very flexible since it can easily coupled with different deterministic schemes, even with high-resolution features. Flexibility and performances of the present method are demonstrated on various numerical test cases (algebraic functions and ordinary differential equations), including partial differential equations, both linear and non-linear, in presence of randomness. The efficiency of the proposed strategy for solving stochastic linear advection and Burgers equation is shown by comparison with some classical techniques for UQ, namely Monte Carlo or the non-intrusive polynomial chaos methods. Keywords Multiresolution Uncertainty quantification Adaptive grid ENO MUSCL Hyperbolic conservation laws