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Article
Algorithm Refinement for Stochastic Partial Diffential Equations
AIP Conference Proceedings (2003)
  • Alejandro Garcia, San Jose State University
  • Francis Alexander, Los Alamos National Laboratory
  • Daniel Tartakovsky, Los Alamos National Laboratory
Abstract
We construct a hybrid particle/continuum algorithm for linear diffusion in the fluctuating hydrodynamic limit. The particles act as independent random walkers and the fluctuating diffusion equation is solved by a finite difference scheme. At the interface between the particle and continuum computations the coupling is by flux matching, and yields exact mass conservation. This approach is an extension of Adaptive Mesh and Algorithm Refinement [J. Comp. Phys. 154 134 (1999)] to stochastic partial differential equations. We present results from a variety of numerical tests, and in all cases the mean and variance of density are obtained correctly by the stochastic hybrid algorithm. A non-stochastic hybrid (i.e., using only deterministic continuum fluxes) results in the correct mean density, but the variance is diminished except in particle regions away from the interface. Extensions of the approach to other applications are discussed.
Keywords
  • refinement,
  • differential equations
Publication Date
2003
Citation Information
Alejandro Garcia, Francis Alexander and Daniel Tartakovsky. "Algorithm Refinement for Stochastic Partial Diffential Equations" AIP Conference Proceedings Vol. 663 (2003)
Available at: http://works.bepress.com/alejandro_garcia1/31/