A fully adaptive multilevel stochastic collocation strategy for solving elliptic PDEs with random data

Uloženo v:
Podrobná bibliografie
Název: A fully adaptive multilevel stochastic collocation strategy for solving elliptic PDEs with random data
Autoři: Lang, Jens, Scheichl, Robert, Silvester, David
Zdroj: Lang, J, Scheichl, R & Silvester, D 2020, 'A fully adaptive multilevel stochastic collocation strategy for solving elliptic PDEs with random data', Journal of Computational Physics, vol. 419, 109692. https://doi.org/10.1016/j.jcp.2020.109692
Rok vydání: 2020
Sbírka: The University of Manchester: Research Explorer - Publications
Témata: Adaptivity, High-dimensional approximation, Multilevel methods, Sparse grids, Stochastic collocation, Uncertainty quantification
Popis: We propose and analyse a fully adaptive strategy for solving elliptic PDEs with random data in this work. A hierarchical sequence of adaptive mesh refinements for the spatial approximation is combined with adaptive anisotropic sparse Smolyak grids in the stochastic space in such a way as to minimize the computational cost. The novel aspect of our strategy is that the hierarchy of spatial approximations is sample dependent so that the computational effort at each collocation point can be optimised individually. We outline a rigorous analysis for the convergence and computational complexity of the adaptive multilevel algorithm and we provide optimal choices for error tolerances at each level. Two numerical examples demonstrate the reliability of the error control and the significant decrease in the complexity that arises when compared to single level algorithms and multilevel algorithms that employ adaptivity solely in the spatial discretisation or in the collocation procedure.
Druh dokumentu: article in journal/newspaper
Popis souboru: application/pdf
Jazyk: English
DOI: 10.1016/j.jcp.2020.109692
Dostupnost: https://research.manchester.ac.uk/en/publications/c08deffa-734b-475f-a4f8-ac606656201f
https://doi.org/10.1016/j.jcp.2020.109692
https://pure.manchester.ac.uk/ws/files/172960384/AMLUQ_LSS2020_final.pdf
http://www.scopus.com/inward/record.url?scp=85087588525&partnerID=8YFLogxK
Rights: info:eu-repo/semantics/openAccess
Přístupové číslo: edsbas.1029C3D1
Databáze: BASE
Buďte první, kdo okomentuje tento záznam!
Nejprve se musíte přihlásit.