A bi-fidelity stochastic collocation method for transport equations with diffusive scaling and multi-dimensional random inputs

Liu Liu, Lorenzo Pareschi, Xueyu Zhu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this paper, we consider the development of efficient numerical methods for linear transport equations with random parameters and under the diffusive scaling. We extend to the present case the bi-fidelity stochastic collocation method introduced in [33,50,51]. For the high-fidelity transport model, the asymptotic-preserving scheme [29] is used for each stochastic sample. We employ the simple two-velocity Goldstein-Taylor equation as low-fidelity model to accelerate the convergence of the uncertainty quantification process. The choice is motivated by the fact that both models, high fidelity and low fidelity, share the same diffusion limit. Speed-up is achieved by proper selection of the collocation points and reasonable approximation of the high-fidelity solution. Extensive numerical experiments are conducted to show the efficiency and accuracy of the proposed method, even in non diffusive regimes, with empirical error bound estimations as studied in [16].

Original languageEnglish
Article number111252
JournalJournal of Computational Physics
Volume462
DOIs
StatePublished - 1 Aug 2022
Externally publishedYes

Keywords

  • Asymptotic-preserving schemes
  • Bi-fidelity method
  • Diffusive scaling
  • Goldstein-Taylor model
  • Transport equations
  • Uncertainty quantification

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