An adaptive multi-element generalized polynomial chaos method for stochastic differential equations

  • Authors:

  • Xiaoliang Wan;George Em Karniadakis

  • Affiliations:

  • Division of Applied Mathematics, Center for Fluid Mechanics, Brown University, 182 George Street, Box F, Providence, RI 02912, USA;Division of Applied Mathematics, Center for Fluid Mechanics, Brown University, 182 George Street, Box F, Providence, RI 02912, USA

  • Venue:
  • Journal of Computational Physics
  • Year:
  • 2005

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Abstract

We formulate a Multi-Element generalized Polynomial Chaos (ME-gPC) method to deal with long-term integration and discontinuities in stochastic differential equations. We first present this method for Legendre-chaos corresponding to uniform random inputs, and subsequently we generalize it to other random inputs. The main idea of ME-gPC is to decompose the space of random inputs when the relative error in variance becomes greater than a threshold value. In each subdomain or random element, we then employ a generalized polynomial chaos expansion. We develop a criterion to perform such a decomposition adaptively, and demonstrate its effectiveness for ODEs, including the Kraichnan-Orszag three-mode problem, as well as advection-diffusion problems. The new method is similar to spectral element method for deterministic problems but with h-p discretization of the random space.